. Best and . Award, Deep learning for estimation of human semantic traits, Journée des Doctorants d'Orange Labs, 2016.

.. Prédiction-du-genre-et-de-l-de-visages, Âge à Partir d'Images, p.157

C. Générale and .. , 173 CHAPITRE 8. RÉSUMÉ ÉTENDU EN FRANÇAIS présenté une nouvelle méthode permettant d'appliquer GA-cGAN à l'édition du genre et de l'âge dans les images de visages en préservant l'identité de l'image originale

. Dans-le-travail-futur, Cela concerne non seulement la conception de CNNs pour l'estimation de ces modalités, mais aussi la minimisation de leur impact sur nos modèles de la classification du genre et de l'estimation de l'âge (par exemple, nous avons remarqué que notre meilleure CNN de l'âge n'est pas très robuste aux variations de l'expression faciale). Autrement, nous planifions d'augmenter la résolution d'images de visages synthétisées et éditées par notre GAcGAN (pour le moment, il s'agit de seulement 64x64 pixels) Pour cela, nous planifions de généraliser les conclusions de cette thèse sur les autres modalités faciales

[. Bibliography, S. Arjovsky, L. Chintala, and . Bottou, Wasserstein gan, CoRR abs, p.7875, 1701.

[. Amos, B. Ludwiczuk, and M. Satyanarayanan, OpenFace: A generalpurpose face recognition library with mobile applications, 2016.

[. Baccouche, F. Mamalet, C. Wolf, C. Garcia, and A. Baskurt, Spatio-Temporal Convolutional Sparse Auto-Encoder for Sequence Classification, Procedings of the British Machine Vision Conference 2012, 2012.
DOI : 10.5244/C.26.124

URL : https://hal.archives-ouvertes.fr/hal-01353046

M. Baccouche, Apprentissage neuronal de caractéristiques spatio-temporelles pour la classification automatique de séquences vidéo, 2013.

J. Bar+13-]-oren-barkan, L. Weill, H. Wolf, and . Aronowitz, Fast high dimensional vector multiplication face recognition, Proceedings of International Conference on Computer Vision, 2013.

J. Bekios-calfa, M. Jose, L. Buenaposada, and . Baumela, Revisiting Linear Discriminant Techniques in Gender Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.33, issue.4, pp.858-864, 2011.
DOI : 10.1109/TPAMI.2010.208

URL : http://www.dia.fi.upm.es/%7Epcr/publications/pami2011.pdf

[. Bengio, A. Courville, and P. Vincent, Representation Learning: A Review and New Perspectives, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.8, pp.1798-1828, 2013.
DOI : 10.1109/TPAMI.2013.50

URL : http://www.cs.princeton.edu/courses/archive/spring13/cos598C/Representation Learning - A Review and New Perspectives.pdf

[. Bilinski, A. Dantcheva, and F. Brémond, Can a Smile Reveal Your Gender?, 2016 International Conference of the Biometrics Special Interest Group (BIOSIG), 2016.
DOI : 10.1109/BIOSIG.2016.7736914

URL : https://hal.archives-ouvertes.fr/hal-01387134

N. Peter, . Belhumeur, W. David, . Jacobs, J. David et al., Localizing parts of faces using a consensus of exemplars, In: Transactions on Pattern Analysis and Machine Intelligence, vol.3512, pp.2930-2940, 2013.

[. Bengio, Learning Deep Architectures for AI, Foundations and trends in Machine Learning, pp.1-127, 2009.
DOI : 10.1561/2200000006

URL : http://www.iro.umontreal.ca/~bengioy/papers/ftml.pdf

[. Bengio, E. Laufer, G. Alain, and J. Yosinski, Deep generative stochastic networks trainable by backprop, Proceedings of International Conference on Machine Learning, 2014.

[. Bourdev, S. Maji, and J. Malik, Describing people: A poselet-based approach to attribute classification, 2011 International Conference on Computer Vision, 2011.
DOI : 10.1109/ICCV.2011.6126413

URL : http://www.eecs.berkeley.edu/Research/Projects/CS/vision/shape/attributes-poselets-iccv11.pdf

[. Burt, I. David, and . Perrett, Perception of Age in Adult Caucasian Male Faces: Computer Graphic Manipulation of Shape and Colour Information, Proceedings of the Royal Society B: Biological Sciences, vol.259, issue.1355, pp.1355-137, 1995.
DOI : 10.1098/rspb.1995.0021

[. Baluja, A. Henry, and . Rowley, Boosting Sex Identification Performance, International Journal of Computer Vision, vol.20, issue.1, pp.111-119, 2007.
DOI : 10.1007/s11263-006-8910-9

URL : http://www.cs.cmu.edu/~har/ijcv2007-sex.pdf

[. Bengio, P. Simard, and P. Frasconi, Learning long-term dependencies with gradient descent is difficult, IEEE transactions on neural networks 5, pp.157-166, 1994.
DOI : 10.1109/72.279181

URL : http://www.research.microsoft.com/~patrice/PDF/long_term.pdf

H. Richard, P. Byrd, J. Lu, C. Nocedal, and . Zhu, A limited memory algorithm for bound constrained optimization, In: SIAM Journal on Scientific Computing, vol.165, pp.1190-1208, 1995.

D. Cai, X. He, J. Han, and H. Zhang, Orthogonal Laplacianfaces for Face Recognition, IEEE Transactions on Image Processing, vol.15, issue.11, pp.3608-3614, 2006.
DOI : 10.1109/TIP.2006.881945

URL : http://people.cs.uchicago.edu/%7Exiaofei/journal-4.pdf

[. Cao, M. Dikmen, Y. Fu, S. Thomas, and . Huang, Gender recognition from body, Proceeding of the 16th ACM international conference on Multimedia, MM '08, 2008.
DOI : 10.1145/1459359.1459470

URL : http://www.ifp.illinois.edu/~cao4/papers/mm08_gender.pdf

[. Chaabouni, J. Benois-pineau, and C. B. Amar, Transfer learning with deep networks for saliency prediction in natural video, 2016 IEEE International Conference on Image Processing (ICIP), 2016.
DOI : 10.1109/ICIP.2016.7532629

[. Chang, C. Chen, and Y. Hung, Ordinal hyperplanes ranker with cost sensitivities for age estimation, CVPR 2011, 2011.
DOI : 10.1109/CVPR.2011.5995437

C. Bor-chun-chen, . Chen, H. Winston, and . Hsu, Cross-age reference coding for age-invariant face recognition and retrieval, Proceedings of European Conference on Computer Vision, 2014.

F. Timothy, . Cootes, J. Gareth, . Edwards, J. Christopher et al., Active appearance models, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.236, pp.681-685, 2001.

W. Gao, WLD: A robust local image descriptor, In: Transactions on Pattern Analysis and Machine Intelligence, vol.329, pp.1705-1720, 2010.

[. Ciodaro, . Deva, D. Jm-de-seixas, and . Damazio, Online particle detection with Neural Networks based on topological calorimetry information, Journal of Physics: Conference Series, vol.368, issue.1, p.12030, 2012.
DOI : 10.1088/1742-6596/368/1/012030

URL : http://iopscience.iop.org/article/10.1088/1742-6596/368/1/012030/pdf

M. Refik-can-malli and . Aygun, Apparent Age Estimation Using Ensemble of Deep Learning Models, Proceedings of Computer Vision and Pattern Recognition Workshops, 2016.

M. Collins, J. Zhang, P. Miller, and H. Wang, Full body image feature representations for gender profiling, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, 2009.
DOI : 10.1109/ICCVW.2009.5457467

[. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu et al., Natural language processing (almost) from scratch, Journal of Machine Learning Research, vol.12, pp.2493-2537, 2011.

A. Canziani, A. Paszke, and E. Culurciello, An Analysis of Deep Neural Network Models for Practical Applications, CoRR abs/1605, p.7678, 2016.

C. Chen and A. Ross, Evaluation of gender classification methods on thermal and near-infrared face images, 2011 International Joint Conference on Biometrics (IJCB), 2011.
DOI : 10.1109/IJCB.2011.6117544

[. Castrillón-santana, M. D. Marsico, M. Nappi, and D. Riccio, MEG: Texture operators for multi-expert gender classification, Computer Vision and Image Understanding, vol.156, 2016.
DOI : 10.1016/j.cviu.2016.09.004

[. Castrillón-santana, J. Lorenzo-navarro, and E. Ramón-balmaseda, Descriptors and regions of interest fusion for gender classification in the wild. Comparison and combination with CNNs, pp.6838-6840, 2016.

[. Clevert, T. Unterthiner, and S. Hochreiter, Fast and accurate deep network learning by exponential linear units (elus), 2016.

C. Cortes and V. Vapnik, Support-vector networks, Machine learning, pp.273-297, 1995.
DOI : 10.1007/BF00994018

[. Dantcheva, C. Velardo, D. Angela, J. Angelo, and . Dugelay, Bag of soft biometrics for person identification, Multimedia Tools and Applications 51, pp.739-777, 2011.
DOI : 10.1109/TPAMI.2003.1251144

[. Danisman, I. M. Bilasco, and J. Martinet, Boosting gender recognition performance with a fuzzy inference system, Expert Systems with Applications, vol.42, issue.5, pp.2772-2784, 2015.
DOI : 10.1016/j.eswa.2014.11.023

URL : https://hal.archives-ouvertes.fr/hal-01111681

[. Deng, P. Luo, C. C. Loy, and X. Tang, Pedestrian Attribute Recognition At Far Distance, Proceedings of the ACM International Conference on Multimedia, MM '14, pp.789-792
DOI : 10.1007/978-3-642-28598-1

[. Dantcheva, P. Elia, and A. Ross, What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics, IEEE Transactions on Information Forensics and Security, vol.11, issue.3, pp.441-467, 2016.
DOI : 10.1109/TIFS.2015.2480381

URL : https://hal.archives-ouvertes.fr/hal-01247885

H. Dibeklio?-glu, T. Gevers, A. A. Salah, and R. Valenti, A smile can reveal your age: Enabling facial dynamics in age estimation, Proceedings of ACM Multimedia, 2012.

A. Dosovitskiy and V. Koltun, Learning to act by predicting the future, Proceedings of International Conference on Learning Representations, 2017.

[. Dong, Y. Liu, and S. Lian, Automatic age estimation based on deep learning algorithm, Neurocomputing, vol.187, pp.4-10, 2016.
DOI : 10.1016/j.neucom.2015.09.115

C. Dong, C. C. Loy, K. He, and X. Tang, Learning a Deep Convolutional Network for Image Super-Resolution, Proceedings of European Conference on Computer Vision, 2014.
DOI : 10.1007/978-3-319-10593-2_13

[. Dalal and B. Triggs, Histograms of Oriented Gradients for Human Detection, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)
DOI : 10.1109/CVPR.2005.177

URL : https://hal.archives-ouvertes.fr/inria-00548512

I. Vincent-dumoulin, B. Belghazi, A. Poole, M. Lamb, O. Arjovsky et al., Adversarially learned inference, Proceedings of International Conference on Learning Representations, 2017.

[. Elagouni, C. Garcia, F. Mamalet, and P. Sébillot, Combining Multi-scale Character Recognition and Linguistic Knowledge for Natural Scene Text OCR, 2012 10th IAPR International Workshop on Document Analysis Systems, 2012.
DOI : 10.1109/DAS.2012.26

URL : https://hal.archives-ouvertes.fr/hal-00753908

[. Escalera, J. Fabian, P. Pardo, and X. Baro, ChaLearn Looking at People 2015: Apparent Age and Cultural Event Recognition Datasets and Results, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW)
DOI : 10.1109/ICCVW.2015.40

URL : https://hal.archives-ouvertes.fr/hal-01381160

[. Escalera, M. Torres, B. Martinez, and X. Baro, ChaLearn Looking at People and Faces of the World: Face Analysis Workshop and Challenge, Proceedings of Computer Vision and Pattern Recognition Workshops, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01381152

J. Fellous, Gender discrimination and prediction on the basis of facial metric information, Vision Research, vol.37, issue.14, pp.1961-1973, 1997.
DOI : 10.1016/S0042-6989(97)00010-2

Y. Fu, G. Guo, S. Thomas, and . Huang, Age synthesis and estimation via faces: A survey, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.3211, pp.1955-1976, 2010.

P. Fischer, A. Dosovitskiy, E. Ilg, P. Häusser, C. Haz?rba¸shaz?rba¸s et al., Flownet: Learning optical flow with convolutional networks, Proceedings of International Conference on Computer Vision, 2015.

[. Fogel and D. Sagi, Gabor filters as texture discriminator, Biological Cybernetics, vol.61, issue.2, pp.103-113, 1989.
DOI : 10.1007/BF00204594

Y. Freund, E. Robert, and . Schapire, A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting, Journal of computer and system sciences 55, pp.119-139, 1997.
DOI : 10.1006/jcss.1997.1504

J. Gauthier, Conditional generative adversarial nets for convolutional face generation, Class Project for Stanford CS231N: Convolutional Neural Networks for Visual Recognition, 2014.

[. Goodfellow, Y. Bengio, and A. Courville, Deep learning, 2016.

[. Gray, S. Brennan, and H. Tao, Evaluating appearance models for recognition , reacquisition, and tracking, Proceedings of Performance Evaluation for Tracking and Surveillance Workshop. Rio de Janeiro, 2007.

C. Garcia and M. Delakis, Convolutional face finder: a neural architecture for fast and robust face detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.11, pp.1408-1423, 2004.
DOI : 10.1109/TPAMI.2004.97

A. Patricia, . George, J. Graham, and . Hole, Factors influencing the accuracy of age estimates of unfamiliar faces, In: Perception, vol.24, issue.9, pp.1059-1073, 1995.

[. Girshick, J. Donahue, T. Darrell, and J. Malik, Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
DOI : 10.1109/CVPR.2014.81

URL : http://arxiv.org/pdf/1311.2524

A. Beatrice, . Golomb, T. David, T. J. Lawrence, and . Sejnowski, SEXNET: A Neural Network Identifies Sex From Human Faces, Proceedings of Advances in Neural Information Processing Systems, 1990.

G. Guo and G. Mu, Human age estimation: What is the influence across race and gender? In: Proceedings of Computer Vision and Pattern Recognition Workshops, 2010.

G. Guo and G. Mu, Simultaneous dimensionality reduction and human age estimation via kernel partial least squares regression, CVPR 2011, 2011.
DOI : 10.1109/CVPR.2011.5995404

URL : http://www.cs.wisc.edu/%7Egdguo/myPapersOnWeb/CVPR11Guo.pdf

G. Guo and G. Mu, A framework for joint estimation of age, gender and ethnicity on a large database, Image and Vision Computing, vol.32, issue.10, pp.761-770, 2014.
DOI : 10.1016/j.imavis.2014.04.011

[. Guo, G. Mu, and Y. Fu, Gender from Body: A Biologically-Inspired Approach with Manifold Learning, Proceedings of Asian Conference on Computer Vision. Xi'an, 2010.
DOI : 10.1007/978-3-642-12297-2_23

[. Graves, A. Mohamed, and G. Hinton, Speech recognition with deep recurrent neural networks, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013.
DOI : 10.1109/ICASSP.2013.6638947

URL : http://learning.cs.toronto.edu/~hinton/absps/RNN13.pdf

A. Gunay, V. Vasif, and . Nabiyev, Automatic Detection of Anthropometric Features from Facial Images, 2007 IEEE 15th Signal Processing and Communications Applications, 2007.
DOI : 10.1109/SIU.2007.4298656

A. Gunay, V. Vasif, and . Nabiyev, Automatic age classification with LBP, 2008 23rd International Symposium on Computer and Information Sciences, 2008.
DOI : 10.1109/ISCIS.2008.4717926

I. Goodfellow, J. Pouget-abadie, M. Mirza, B. Xu, D. Warde-farley et al., Generative adversarial nets, Proceedings of advances in Neural Information Processing Systems, 2015.

I. Goodfellow, NIPS 2016 Tutorial: Generative Adversarial Networks, p.160, 1701.

[. Gonzalez-sosa, A. Dantcheva, R. Vera-rodriguez, J. Dugelay, F. Brémond et al., Image-based gender estimation from body and face across distances, 2016 23rd International Conference on Pattern Recognition (ICPR), 2016.
DOI : 10.1109/ICPR.2016.7900104

URL : https://hal.archives-ouvertes.fr/hal-01384324

A. Graves and J. Schmidhuber, Framewise phoneme classification with bidirectional LSTM and other neural network architectures, Neural Networks, vol.18, issue.5-6, pp.602-610, 2005.
DOI : 10.1016/j.neunet.2005.06.042

URL : http://www6.in.tum.de/pub/Main/Publications/Graves2005a.pdf

[. Guo, Y. Fu, R. Charles, . Dyer, S. Thomas et al., Image-based human age estimation by manifold learning and locally adjusted robust regression, In: Transactions on Image Processing, vol.177, pp.1178-1188, 2008.

[. Guo, G. Mu, Y. Fu, S. Thomas, and . Huang, Human age estimation using bio-inspired features, 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009.
DOI : 10.1109/CVPR.2009.5206681

[. Guo, L. Zhang, Y. Hu, X. He, and J. Gao, MS-Celeb-1M: Challenge of Recognizing One Million Celebrities in the Real World, Electronic Imaging, vol.2016, issue.11, pp.1-6, 2016.
DOI : 10.2352/ISSN.2470-1173.2016.11.IMAWM-463

G. Guo, Human Age Estimation and Sex Classification, pp.101-131, 2012.
DOI : 10.1007/978-3-642-28598-1_4

[. Gurpinar, H. Kaya, H. Dibeklioglu, and A. Salah, Kernel ELM and CNN Based Facial Age Estimation, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2016.
DOI : 10.1109/CVPRW.2016.103

G. Guo and X. Wang, A study on human age estimation under facial expression changes, Proceedings of Computer Vision and Pattern Recognition, 2012.

[. Gutta, H. Wechsler, and P. Phillips, Gender and ethnic classification of face images, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition, 1998.
DOI : 10.1109/AFGR.1998.670948

[. Geng, C. Yin, and Z. Zhou, Facial age estimation by learning from label distributions, In: Transactions on Pattern Analysis and Machine Intelligence, vol.3510, pp.2401-2412, 2013.

[. Geng, Z. Zhou, and K. Smith-miles, Automatic Age Estimation Based on Facial Aging Patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.12, pp.2234-2240, 2007.
DOI : 10.1109/TPAMI.2007.70733

URL : http://dro.deakin.edu.au/eserv/DU:30007652/geng-automaticage-2007.pdf

H. Han, C. Otto, X. Liu, K. Anil, and . Jain, Demographic estimation from face images: Human vs. machine performance " . In: Transactions on pattern analysis and machine intelligence 37, pp.1148-1161, 2015.
DOI : 10.1109/tpami.2014.2362759

[. He, X. Zhang, S. Ren, and J. Sun, Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification, 2015 IEEE International Conference on Computer Vision (ICCV), 2015.
DOI : 10.1109/ICCV.2015.123

URL : http://arxiv.org/pdf/1502.01852

[. He, X. Zhang, S. Ren, and J. Sun, Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
DOI : 10.1109/CVPR.2016.90

URL : http://arxiv.org/pdf/1512.03385

J. Ho and S. Ermon, Generative adversarial imitation learning, Proceedings of advances in Neural Information Processing Systems, p.2016

G. Hinton, L. Deng, D. Yu, E. George, A. Dahl et al., Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups, IEEE Signal Processing Magazine, vol.29, issue.6, pp.82-97, 2012.
DOI : 10.1109/MSP.2012.2205597

A. James, . Hanley, J. Barbara, and . Mcneil, The meaning and use of the area under a receiver operating characteristic (ROC) curve, In: Radiology, vol.1431, pp.29-36, 1982.

T. Huynh, R. Min, and J. Dugelay, An Efficient LBP-Based Descriptor for Facial Depth Images Applied to Gender Recognition Using RGB-D Face Data, Proceedings of Asian Conference on Computer Vision, 2012.
DOI : 10.1007/978-3-642-37410-4_12

[. Hochreiter, Y. Bengio, P. Frasconi, and J. Schmidhuber, Gradient flow in recurrent nets: the difficulty of learning long-term dependencies, 2001.

E. Geoffrey, S. Hinton, Y. Osindero, and . Teh, A fast learning algorithm for deep belief nets, Neural computation, vol.187, pp.1527-1554, 2006.

A. Hadid and M. Pietikäinen, Manifold Learning for Gender Classification from Face Sequences, Biometrics, pp.82-91, 2009.
DOI : 10.1109/TPAMI.2007.1110

E. Geoffrey, . Hinton, R. Ruslan, and . Salakhutdinov, Reducing the dimensionality of data with neural networks " . In: science 313, pp.504-507, 2006.

[. Hochreiter and J. Schmidhuber, Long Short-Term Memory, Neural Computation, vol.4, issue.8, pp.1735-1780, 1997.
DOI : 10.1016/0893-6080(88)90007-X

E. Geoffrey, T. J. Hinton, . Sejnowski, H. David, and . Ackley, Boltzmann machines: Constraint satisfaction networks that learn, 1984.

[. Hsu, Behind Deep Blue: Building the computer that defeated the world chess champion, 2002.

S. Y. , D. Hu, B. Jou, A. Jaech, and M. Savvides, Fusion of regionbased representations for gender identification, Proceedings of International Joint Conference on Biometrics, 2011.

B. Gary, M. Huang, T. Ramesh, E. Berg, and . Learned-miller, Labeled faces in the wild: A database for studying face recognition in unconstrained environments, 2007.

Z. Huo, X. Yang, C. Xing, Y. Zhou, P. Hou et al., Deep Age Distribution Learning for Apparent Age Estimation, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2016.
DOI : 10.1109/CVPRW.2016.95

[. Han, H. Ugail, and I. Palmer, Gender Classification Based on 3D Face Geometry Features Using SVM, 2009 International Conference on CyberWorlds, 2009.
DOI : 10.1109/CW.2009.41

M. Yasar-iscan and M. Steyn, The human skeleton in forensic medicine, 2013.

[. Ioffe and C. Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, Proceedings of International Conference on Machine Learning, 2015.

[. Isola, J. Zhu, T. Zhou, and A. A. Efros, Image-to-Image Translation with Conditional Adversarial Networks, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
DOI : 10.1109/CVPR.2017.632

URL : http://arxiv.org/pdf/1611.07004

S. Jia and N. Cristianini, Learning to classify gender from four million images, Pattern Recognition Letters, vol.58
DOI : 10.1016/j.patrec.2015.02.006

K. Anil, . Jain, C. Sarat, K. Dass, and . Nandakumar, Soft biometric traits for personal recognition systems, Biometric authentication, pp.731-738, 2004.

A. Jain and J. Huang, Integrating independent components and linear discriminant analysis for gender classification, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings., 2004.
DOI : 10.1109/AFGR.2004.1301524

J. Johnson, A. Karpathy, and L. Fei-fei, DenseCap: Fully Convolutional Localization Networks for Dense Captioning, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
DOI : 10.1109/CVPR.2016.494

URL : http://arxiv.org/pdf/1511.07571

[. Joachims, Making large-scale SVM learning practical, 1998.

[. Jordan, Learning in graphical models, 1998.
DOI : 10.1007/978-94-011-5014-9

[. Juefei-xu, E. Verma, and P. Goel, Anisha Cherodian and Marios Savvides DeepGender: Occlusion and Low Resolution Robust Facial Gender Classification via Progressively Trained Convolutional Neural Networks with Attention, Proceedings of Computer Vision and Pattern Recognition Workshops, 2016.
DOI : 10.1109/cvprw.2016.24

[. Jégou, M. Douze, C. Schmid, and P. Pérez, Aggregating local descriptors into a compact image representation, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010.
DOI : 10.1109/CVPR.2010.5540039

A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar et al., Large-Scale Video Classification with Convolutional Neural Networks, 2014 IEEE Conference on Computer Vision and Pattern Recognition
DOI : 10.1109/CVPR.2014.223

URL : http://www.cs.cmu.edu/~rahuls/pub/cvpr2014-deepvideo-rahuls.pdf

[. Kingma and J. Ba, Adam: A method for stochastic optimization, CoRR abs, p.6980, 1412.

P. Korshunov and T. Ebrahimi, Using face morphing to protect privacy, 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, 2013.
DOI : 10.1109/AVSS.2013.6636641

URL : http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.352.3167&rep=rep1&type=pdf

A. Krizhevsky, E. Geoffrey, and . Hinton, Using very deep autoencoders for contentbased image retrieval, Proceedings of European Symposium on Artificial Neural Networks, 2011.

[. Khan, A. Majid, M. Anwar, and . Mirza, Combination and optimization of classifiers in gender classification using genetic programming, International Journal of Knowledge-based and Intelligent Engineering Systems, vol.9, issue.1, pp.1-11, 2005.
DOI : 10.3233/KES-2005-9101

M. Rolf, . Koch, H. Markus, . Gross, R. Friedrich et al., Simulating facial surgery using finite element models, Proceedings of Computer Graphics and Interactive Techniques, 1996.

A. Krizhevsky, I. Sutskever, E. Geoffrey, and . Hinton, ImageNet classification with deep convolutional neural networks, Proceedings of advances in Neural Information Processing Systems. Lake Tahoe
DOI : 10.1162/neco.2009.10-08-881

URL : http://dl.acm.org/ft_gateway.cfm?id=3065386&type=pdf

[. Kemelmacher-shlizerman, S. Suwajanakorn, M. Steven, and . Seitz, Illuminationaware age progression, Proceedings of Computer Vision and Pattern Recognition, 2014.

P. Diederik, M. Kingma, and . Welling, Auto-encoding variational bayes, Proceedings of International Conference on Learning Representations, 2014.

A. Boesen, L. Larsen, H. Søren-kaae-sønderby, O. Larochelle, and . Winther, Autoencoding beyond pixels using a learned similarity metric, Proceedings of International Conference on Machine Learning, 2016.

R. Layne, M. Timothy, S. Hospedales, Q. Gong, and . Mary, Person Re-identification by Attributes, Procedings of the British Machine Vision Conference 2012, 2012.
DOI : 10.5244/C.26.24

URL : http://www.bmva.org/bmvc/2012/BMVC/paper024/paper024.pdf

Y. Lbh15-]-yann-lecun, G. Bengio, and . Hinton, Deep learning, Nature, vol.5217553, pp.436-444, 2015.

M. Lin, Q. Chen, and S. Yan, Network in network, Proceedings of International Conference on Learning Representations, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01460127

A. Lanitis, C. Draganova, and C. Christodoulou, Comparing Different Classifiers for Automatic Age Estimation, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol.34, issue.1, pp.621-628, 2004.
DOI : 10.1109/TSMCB.2003.817091

URL : http://roar.uel.ac.uk/608/1/Lanitis%2C%20A%20%282004%29%20IEEE%20TSMC%2034%20%281%29%20621-8.pdf

W. Hubbard, D. Lawrence, and . Jackel, Backpropagation applied to handwritten zip code recognition, In: Neural computation, vol.1, issue.4, pp.541-551, 1989.

L. Lec+98-]-yann-lecun, Y. Bottou, P. Bengio, and . Haffner, Gradient-based learning applied to document recognition, pp.2278-2324, 1998.

Y. Lecun, Une procédure d'apprentissage pour réseau a seuil asymmetrique (a learning scheme for asymmetric threshold networks), Proceedings of Cognitiva 85, 1985.

[. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham et al., Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
DOI : 10.1109/CVPR.2017.19

URL : http://arxiv.org/pdf/1609.04802

G. Levi and T. Hassner, Age and gender classification using convolutional neural networks, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
DOI : 10.1109/CVPRW.2015.7301352

[. Loth and M. Iscan, ANTHROPOLOGY| Sex Determination, Encyclopedia of forensic sciences, pp.252-260, 2000.

[. Liang, Y. Wei, X. Shen, Z. Jie, J. Feng et al., Reversible Recursive Instance-Level Object Segmentation, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
DOI : 10.1109/CVPR.2016.75

X. Liu, S. Li, M. Kan, and J. Zhang, AgeNet: Deeply Learned Regressor and Classifier for Robust Apparent Age Estimation, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW), 2015.
DOI : 10.1109/ICCVW.2015.42

Z. Liu, P. Luo, X. Wang, and X. Tang, Deep Learning Face Attributes in the Wild, 2015 IEEE International Conference on Computer Vision (ICCV), 2015.
DOI : 10.1109/ICCV.2015.425

Z. Liu, P. Luo, X. Wang, and X. Tang, Deep Learning Face Attributes in the Wild, 2015 IEEE International Conference on Computer Vision (ICCV), 2015.
DOI : 10.1109/ICCV.2015.425

H. Liu, J. Lu, J. Feng, and J. Zhou, Group-aware deep feature learning for facial age estimation, Pattern Recognition, vol.66, pp.82-94, 2017.
DOI : 10.1016/j.patcog.2016.10.026

E. Learned-miller, B. Gary, A. Huang, H. Roychowdhury, G. Li et al., Labeled faces in the wild: A survey Advances in Face Detection and Facial Image Analysis, pp.189-248, 2016.

G. David and . Lowe, Object recognition from local scale-invariant features, Proceedings of Computer Vision and Pattern Recognition, 1999.

[. Long, E. Shelhamer, and T. Darrell, Fully convolutional networks for semantic segmentation, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
DOI : 10.1109/CVPR.2015.7298965

J. Lu and Y. Tan, Gait-based human age estimation, IEEE Transactions on Information Forensics and Security, vol.54, pp.761-770, 2010.
DOI : 10.1109/icassp.2010.5495473

A. Lanitis, C. J. Taylor, F. Timothy, and . Cootes, Toward automatic simulation of aging effects on face images, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.4, pp.442-455, 2002.
DOI : 10.1109/34.993553

J. Lu, J. Hu, V. E. Liong, X. Zhou, A. Bottino et al., The fg 2015 kinship verification in the wild evaluation, Proceedings of Automatic Face and Gesture Recognition Workshops, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01158942

P. Luc, C. Couprie, S. Chintala, and J. Verbeek, Semantic segmentation using adversarial networks, Proceedings of advances in Neural Information Processing Systems Workshops, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01398049

[. Luu, K. Ricanek, D. Tien, . Bui, Y. Ching et al., Age estimation using Active Appearance Models and Support Vector Machine regression, 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems, 2009.
DOI : 10.1109/BTAS.2009.5339053

[. Luu, K. Seshadri, M. Savvides, D. Tien, . Bui et al., Contourlet appearance model for facial age estimation, 2011 International Joint Conference on Biometrics (IJCB), 2011.
DOI : 10.1109/IJCB.2011.6117601

URL : http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.352.5823&rep=rep1&type=pdf

C. Liu and H. Wechsler, Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition, In: Transactions on Image Processing, vol.11, issue.4, pp.467-476, 2002.

[. Liu, S. Yan, and C. Kuo, Age Estimation via Grouping and Decision Fusion, IEEE Transactions on Information Forensics and Security, vol.10, issue.11, pp.11-2408, 2015.
DOI : 10.1109/TIFS.2015.2462732

J. Mansanet, A. Albiol, and R. Paredes, Local Deep Neural Networks for gender recognition, Pattern Recognition Letters, vol.70, pp.80-86, 2016.
DOI : 10.1016/j.patrec.2015.11.015

URL : https://riunet.upv.es/bitstream/10251/84826/3/main_plain.pdf

[. Mathias, R. Benenson, M. Pedersoli, and L. Van-gool, Face Detection without Bells and Whistles, Proceedings of European Conference on Computer Vision, 2014.
DOI : 10.1007/978-3-319-10593-2_47

M. Mathieu, C. Couprie, and Y. Lecun, Deep multi-scale video prediction beyond mean square error, Proceedings of International Conference on Learning Representations, 2016.

G. Abdel-rahman-mohamed, G. Dahl, and . Hinton, Deep belief networks for phone recognition, Proceedings of advances in Neural Information Processing Systems Workshops, 2009.

A. George and . Miller, WordNet: a lexical database for English, Communications of the ACM, vol.3811, pp.39-41, 1995.

M. Mirza and S. Osindero, Conditional generative adversarial nets, Proceedings of advances in Neural Information Processing Systems, 2014.

A. Moeini, H. Moeini, A. M. Safai, and K. Faez, Regression Facial Attribute Classification via simultaneous dictionary learning, Pattern Recognition, vol.62, pp.99-113, 2017.
DOI : 10.1016/j.patcog.2016.08.031

[. Madry-pronobis, Automatic gender recognition based on audiovisual cues, 2009.

S. Warren, W. Mcculloch, and . Pitts, A logical calculus of the ideas immanent in nervous activity " . In: The bulletin of mathematical biophysics 5, pp.115-133, 1943.

M. Minsky and S. Papert, Perceptrons, 1969.

[. Malinowski, M. Rohrbach, and M. Fritz, Ask Your Neurons: A Neural-Based Approach to Answering Questions about Images, 2015 IEEE International Conference on Computer Vision (ICCV), 2015.
DOI : 10.1109/ICCV.2015.9

URL : http://arxiv.org/pdf/1505.01121

[. Moghaddam and M. Yang, Learning gender with support faces, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.5, pp.707-711, 2002.
DOI : 10.1109/34.1000244

URL : http://vision.ai.uiuc.edu/mhyang/papers/pami02b.pdf

Y. Nesterov, A method of solving a convex programming problem with convergence rate O (1/k2), In: Soviet Mathematics Doklady, vol.27, issue.2, pp.372-376, 1983.

[. Neverova, C. Wolf, G. Taylor, and F. Nebout, Multi-scale Deep Learning for Gesture Detection and Localization, Proceedings of European Conference on Computer Vision Workshops, 2014.
DOI : 10.1007/978-3-319-16178-5_33

URL : https://hal.archives-ouvertes.fr/hal-01419792

[. Niu, M. Zhou, L. Wang, X. Gao, and G. Hua, Ordinal Regression with Multiple Output CNN for Age Estimation, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
DOI : 10.1109/CVPR.2016.532

[. Ng, Y. Tay, and B. Goi, A Convolutional Neural Network for Pedestrian Gender Recognition, Advances in Neural Networks. 2013, pp.558-564
DOI : 10.1007/978-3-642-39065-4_67

[. Ozbulak and Y. Aytar, How Transferable are CNNbased Features for Age and Gender Classification, Proceedings of BIOSIG, 2016.
DOI : 10.1109/biosig.2016.7736925

URL : http://arxiv.org/pdf/1610.00134

A. Oord, N. Kalchbrenner, L. Espeholt, O. Vinyals, and A. Graves, Conditional image generation with pixelcnn decoders, Proceedings of advances in Neural Information Processing Systems, 2016.

A. Odena, C. Olah, and J. Shlens, Conditional image synthesis with auxiliary classifier gans, CoRR abs, p.9585, 1610.

[. Ojala, M. Pietikäinen, and D. Harwood, A comparative study of texture measures with classification based on featured distributions, Pattern Recognition, vol.29, issue.1, pp.51-59, 1996.
DOI : 10.1016/0031-3203(95)00067-4

A. Asem, A. Othman, and . Ross, Privacy of Facial Soft Biometrics: Suppressing Gender But Retaining Identity, Proceedings of European Conference on Computer Vision Workshops, 2014.

M. Oren, C. Papageorgiou, P. Sinha, E. Osuna, and T. Poggio, Pedestrian detection using wavelet templates, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1997.
DOI : 10.1109/CVPR.1997.609319

URL : http://www.ai.mit.edu/people/cpapa/publications/ped-wav-temp-cvpr97.ps.gz

[. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros, Context Encoders: Feature Learning by Inpainting, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
DOI : 10.1109/CVPR.2016.278

URL : http://arxiv.org/pdf/1604.07379

P. Paysan, Statistical modeling of facial aging based on 3D scans, 2010.

B. Poggio, R. Brunelli, and T. Poggio, HyberBF networks for gender classification, 1992.

[. Perronnin, Y. Liu, J. Sánchez, and H. Poirier, Large-scale image retrieval with compressed Fisher vectors, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010.
DOI : 10.1109/CVPR.2010.5540009

[. Perarnau, J. Van-de-weijer, B. Raducanu, M. Jose, and . Álvarez, Invertible Conditional GANs for image editing, Proceedings of advances in Neural Information Processing Systems Workshops, 2016.

C. David and . Plaut, Experiments on Learning by Back Propagation, 1986.

M. Omkar, A. Parkhi, A. Vedaldi, and . Zisserman, Deep face recognition, Proceedings of British Machine Vision Conference, 2015.

Q. Sinno-jialin-pan and . Yang, A survey on transfer learning, IEEE Transactions on Knowledge and Data Engineering, vol.2210, pp.1345-1359, 2010.

[. Ramanathan and R. Chellappa, Face Verification across Age Progression, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.3349-3361, 2006.
DOI : 10.1109/CVPR.2005.153

[. Ramanathan and R. Chellappa, Modeling Age Progression in Young Faces, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 1 (CVPR'06), 2006.
DOI : 10.1109/CVPR.2006.187

URL : http://www.umiacs.umd.edu/~ramanath/Ramanathan_cvpr2006.pdf

[. Redmon, S. Divvala, R. Girshick, and A. Farhadi, You Only Look Once: Unified, Real-Time Object Detection, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
DOI : 10.1109/CVPR.2016.91

URL : http://arxiv.org/pdf/1506.02640

[. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele et al., Generative adversarial text to image synthesis, Proceedings of International Conference on Machine Learning, 2016.

H. Taylor and F. Rhodes, Alphonse Bertillon, father of scientific detection, 1956.

E. David, . Rumelhart, E. Geoffrey, . Hinton, J. Ronald et al., Learning internal representations by error propagation, 1985.

[. Romano, J. Isidoro, and P. Milanfar, RAISR: Rapid and Accurate Image Super Resolution, IEEE Transactions on Computational Imaging, vol.3, issue.1, pp.110-125, 2017.
DOI : 10.1109/TCI.2016.2629284

URL : http://doi.org/10.1109/tci.2016.2629284

K. Ricanek, J. , and T. Tesafaye, MORPH: A Longitudinal Image Database of Normal Adult Age-Progression, 7th International Conference on Automatic Face and Gesture Recognition (FGR06)
DOI : 10.1109/FGR.2006.78

A. Radford, L. Metz, and S. Chintala, Unsupervised representation learning with deep convolutional generative adversarial networks, Proceedings of International Conference on Learning Representations, 2016.

[. Roux, F. Mamalet, and C. Garcia, Embedded Convolutional Face Finder, 2006 IEEE International Conference on Multimedia and Expo, 2006.
DOI : 10.1109/ICME.2006.262454

URL : http://www.cecs.uci.edu/~papers/icme06/pdfs/0000285.pdf

A. Arun, K. Ross, . Nandakumar, K. Anil, and . Jain, Handbook of biometrics, 2007.

[. Rosenblatt, The perceptron: A probabilistic model for information storage and organization in the brain., Psychological Review, vol.65, issue.6, p.386, 1958.
DOI : 10.1037/h0042519

A. Duncan, . Rowland, I. David, and . Perrett, Manipulating facial appearance through shape and color, Computer Graphics and Applications, vol.155, pp.70-76, 1995.

M. Riesenhuber and T. Poggio, Hierarchical models of object recognition in cortex, Nature Neuroscience, vol.3, issue.11, pp.1019-1025, 1999.
DOI : 10.1162/neco.1991.3.2.194

T. Sam, . Roweis, K. Lawrence, and . Saul, Nonlinear dimensionality reduction by locally linear embedding, pp.2323-2326, 2000.

[. Rothe, R. Timofte, and L. Van-gool, Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks, International Journal of Computer Vision, vol.30, issue.6, pp.1-14, 2016.
DOI : 10.1109/ICCVW.2015.43

[. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh et al., ImageNet Large Scale Visual Recognition Challenge, International Journal of Computer Vision, vol.1010, issue.1, pp.211-252, 2015.
DOI : 10.1007/978-3-642-15555-0_11

URL : http://dspace.mit.edu/bitstream/1721.1/104944/1/11263_2015_Article_816.pdf

N. Tara, A. Sainath, B. Mohamed, B. Kingsbury, and . Ramabhadran, Deep convolutional neural networks for LVCSR, Proceedings of International Conference on Acoustics, Speech and Signal Processing, 2013.

[. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford et al., Improved techniques for training gans, Proceedings of advances in Neural Information Processing Systems, 2016.

L. Arthur and . Samuel, Some studies in machine learning using the game of checkers, In: IBM Journal of research and development, vol.33, pp.210-229, 1959.

D. Richard, S. Seely, M. Samangooei, . Lee, N. John et al., The university of southampton multi-biometric tunnel and introducing a novel 3d gait dataset Automatic scene text recognition using a convolutional neural network, Proceedings of Biometrics: Theory, Applications and Systems Proceedings of International Conference on Document Analysis and Recognition Workshops, 2007.

L. Shamir, Automatic age estimation by hand photos, In: Computer Science Letters, vol.31, 2011.

C. Shan, Learning local binary patterns for gender classification on real-world face images, Pattern Recognition Letters, vol.33, issue.4, pp.431-437, 2012.
DOI : 10.1016/j.patrec.2011.05.016

URL : https://repository.tudelft.nl/assets/uuid:678436fb-d859-4c4a-8842-f1b4bb5a0fe3/MS-32.590.pdf

[. Shen, W. Lu, S. Shih, and H. Liao, Exemplarbased age progression prediction in children faces, Proceedings of International Symposium on Multimedia. California, 2011.
DOI : 10.1109/ism.2011.28

[. Shihfeng, C. Tu, C. Chuang, and H. Lin, Aging simulation using facial muscle model, 2012 International Conference on Machine Learning and Cybernetics, 2012.
DOI : 10.1109/ICMLC.2012.6359647

[. Shih, Robust gender classification using a precise patch histogram, Pattern Recognition, vol.46, issue.2, pp.519-528, 2013.
DOI : 10.1016/j.patcog.2012.08.003

[. Shu, J. Tang, H. Lai, L. Liu, and S. Yan, Personalized Age Progression with Aging Dictionary, 2015 IEEE International Conference on Computer Vision (ICCV), 2015.
DOI : 10.1109/ICCV.2015.452

URL : http://arxiv.org/pdf/1510.06503

[. Shu, G. Xie, Z. Li, and J. Tang, Age progression: Current technologies and applications, Neurocomputing, vol.208, pp.249-261, 2016.
DOI : 10.1016/j.neucom.2016.01.101

[. Silver, A. Huang, C. J. Maddison, A. Guez, and L. , Mastering the game of Go with deep neural networks and tree search, Nature, vol.34, issue.7587
DOI : 10.3233/ICG-2011-34302

[. Schroff, D. Kalenichenko, and J. Philbin, FaceNet: A unified embedding for face recognition and clustering, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
DOI : 10.1109/CVPR.2015.7298682

URL : http://arxiv.org/pdf/1503.03832

P. Smolensky, Information processing in dynamical systems: Foundations of harmony theory, 1986.

A. Sharif-razavian, H. Azizpour, J. Sullivan, and S. Carlsson, CNN features off-the-shelf: an astounding baseline for recognition, Proceedings of Computer Vision and Pattern Recognition Workshops, 2014.

P. Pérez-san-roman, J. Benois-pineau, and J. Domenger, Saliency Driven Object recognition in egocentric videos with deep CNN: toward application in assistance to Neuroprostheses, Computer Vision and Image Understanding, vol.164, pp.82-91, 2017.
DOI : 10.1016/j.cviu.2017.03.001

[. Srivastava, E. Geoffrey, A. Hinton, and . Krizhevsky, Ilya Sutskever and Ruslan Salakhutdinov Dropout: a simple way to prevent neural networks from overfitting, In: Journal of Machine Learning Research, vol.151, pp.1929-1958, 2014.

[. Sun, W. Zheng, C. Sun, C. Zou, and L. Zhao, Gender Classification Based on Boosting Local Binary Pattern, Proceedings of Advances in Neural Networks, 2006.
DOI : 10.1007/11760023_29

J. Suo, S. Zhu, S. Shan, and X. Chen, A compositional and dynamic model for face aging, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.323, pp.385-401, 2010.

J. Suo, L. Lin, S. Shan, X. Chen, and W. Gao, High-Resolution Face Fusion for Gender Conversion, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, vol.41, issue.2, pp.226-237, 2011.
DOI : 10.1109/TSMCA.2010.2064304

URL : http://vipl.ict.ac.cn/sites/default/files/papers/files/2011_SMC_jlsuo_High Resolution Face Fusion for Gender Conversion.pdf

[. Sutskever, O. Vinyals, V. Quoc, and . Le, Sequence to sequence learning with neural networks, Proceedings of Advances in Neural Information Processing Systems, 2014.

L. Sixt, B. Wild, and T. Landgraf, Rendergan: Generating realistic labeled data, Proceedings of International Conference on Learning Representations Workshops, 2016.

[. Sun, X. Wang, and X. Tang, Deeply learned face representations are sparse, selective, and robust, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
DOI : 10.1109/CVPR.2015.7298907

URL : http://arxiv.org/pdf/1412.1265

K. Simonyan and A. Zisserman, Very deep convolutional networks for largescale image recognition, Proceedings of International Conference on Learning Representations, 2015.

[. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed et al., Going deeper with convolutions, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
DOI : 10.1109/CVPR.2015.7298594

URL : http://arxiv.org/pdf/1409.4842

M. Toews and T. Arbel, Detection, Localization, and Sex Classification of Faces from Arbitrary Viewpoints and under Occlusion, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, issue.9, pp.1567-1581, 2009.
DOI : 10.1109/TPAMI.2008.233

[. Taigman, M. Yang, M. Ranzato, and L. Wolf, DeepFace: Closing the Gap to Human-Level Performance in Face Verification, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
DOI : 10.1109/CVPR.2014.220

C. Wei-ren-tan, H. Seng-chan, K. Aguirre, and . Tanaka, ArtGAN: Artwork Synthesis with Conditional Categorial GANs, p.170203410, 2017.

[. Tiddeman, M. Burt, and D. Perrett, Prototyping and transforming facial textures for perception research, IEEE Computer Graphics and Applications, vol.21, issue.4, pp.42-50, 2001.
DOI : 10.1109/38.946630

URL : https://research-repository.st-andrews.ac.uk/bitstream/10023/1596/1/Tiddeman2001-IEEECompGraph21-Prototyping.pdf

V. Thomas, V. Nitesh, K. W. Chawla, . Bowyer, J. Patrick et al., Learning to predict gender from iris images, 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems, 2007.
DOI : 10.1109/BTAS.2007.4401911

Y. Tian, T. Kanade, F. Jeffrey, and . Cohn, Facial Expression Recognition, pp.487-519, 2011.
DOI : 10.1007/978-0-85729-932-1_19

E. Juan, C. A. Tapia, and . Perez, Gender classification based on fusion of different spatial scale features selected by mutual information from histogram of LBP, intensity, and shape, In: Transactions on Image Forensics and Security, vol.83, pp.488-499, 2013.

[. Taigman, A. Polyak, and L. Wolf, Unsupervised cross-domain image generation, Proceedings of International Conference on Learning Representations, 2017.

[. Ueki, T. Hayashida, and T. Kobayashi, Subspace-based Age-group Classification Using Facial Images under Various Lighting Conditions, 7th International Conference on Automatic Face and Gesture Recognition (FGR06), 2006.
DOI : 10.1109/FGR.2006.102

M. Uricár, V. Franc, and D. Thomas, Akihiro Sugimoto and Václav Hlavác Real-time multi-view facial landmark detector learned by the structured output SVM

M. U?i?á?, V. Franc, and D. Thomas, Multi-view facial landmark detector learned by the Structured Output SVM, Image and Vision Computing, vol.47, pp.45-59, 2016.
DOI : 10.1016/j.imavis.2016.02.004

M. U?i?á?, R. Timofte, R. Rothe, J. Matas, and L. Van-gool, Structured output SVM prediction of apparent age, gender and smile from deep features, Proceedings of Computer Vision and Pattern Recognition Workshops, 2016.

F. Tiago, A. Vieira, A. Bottino, M. D. Laurentini, and . Simone, Detecting siblings in image pairs, The Visual Computer, pp.1333-1345, 2014.

A. Vinyals, S. Toshev, D. Bengio, and . Erhan, Show and tell: A neural image caption generator, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
DOI : 10.1109/CVPR.2015.7298935

URL : http://arxiv.org/pdf/1411.4555

P. Viola and M. Jones, Rapid object detection using a boosted cascade of simple features, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, 2001.
DOI : 10.1109/CVPR.2001.990517

[. Wang, J. Li, W. Yau, and E. Sung, Boosting dense SIFT descriptors and shape contexts of face images for gender recognition, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Workshops, 2010.
DOI : 10.1109/CVPRW.2010.5543238

W. Wang, Z. Cui, Y. Yan, J. Feng, S. Yan et al., Recurrent Face Aging, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
DOI : 10.1109/CVPR.2016.261

S. Wold, K. Esbensen, and P. Geladi, Principal component analysis, Chemometrics and intelligent laboratory systems, pp.1-3, 1987.
DOI : 10.1016/0169-7439(87)80084-9

P. Werbos, Beyond regression: New tools for prediction and analysis in the behavioral sciences, 1974.

[. Wang, R. Guo, and C. Kambhamettu, Deeply-Learned Feature for Age Estimation, 2015 IEEE Winter Conference on Applications of Computer Vision
DOI : 10.1109/WACV.2015.77

Q. Wu, P. Wang, C. Shen, A. Dick, and A. Van, Ask Me Anything: Free-Form Visual Question Answering Based on Knowledge from External Sources, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
DOI : 10.1109/CVPR.2016.500

URL : http://arxiv.org/pdf/1511.06973

J. Ronald, D. Williams, and . Zipser, Gradient-based learning algorithms for recurrent networks and their computational complexity, Backpropagation: Theory, architectures, pp.433-486, 1995.

B. Xiao, X. Yang, H. Zha, Y. Xu, and T. Huang, Metric Learning for Regression Problems and Human Age Estimation, Advances in Multimedia Information Processing, pp.88-99, 2009.
DOI : 10.1007/978-3-642-10467-1_7

Y. Hui, B. Xiong, . Alipanahi, J. Leo, H. Lee et al., The human splicing code reveals new insights into the genetic determinants of disease

Z. Xu, L. Lu, and P. Shi, A hybrid approach to gender classification from face images, Proceedings of International Conference on Pattern Recognition, 2008.

[. Xie, K. Luu, and M. Savvides, A robust approach to facial ethnicity classification on large scale face databases, 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)
DOI : 10.1109/BTAS.2012.6374569

[. Xia, H. Sun, and B. Lu, Multi-view gender classification based on local Gabor binary mapping pattern and support vector machines, Proceedings of International Joint Conference on Neural Networks. Hong Kong, China, 2008.

Z. Yang and H. Ai, Demographic Classification with Local Binary Patterns, Proceedings of International Conference on Biometrics, 2007.
DOI : 10.1007/978-3-540-74549-5_49

URL : http://media.cs.tsinghua.edu.cn/~imagevision/papers/ICB07_demographic.pdf

[. Yang, B. Lin, K. Chang, and C. , Automatic Age Estimation from Face Images via Deep Ranking, Procedings of the British Machine Vision Conference 2015, 2015.
DOI : 10.5244/C.29.55

X. Yang, . Bin-bin, C. Gao, Z. Xing, and . Huo, Deep Label Distribution Learning for Apparent Age Estimation, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW), 2015.
DOI : 10.1109/ICCVW.2015.53

[. Yan, J. Yang, K. Sohn, and H. Lee, Attribute2Image: Conditional Image Generation from Visual Attributes, Proceedings of European Conference on Computer Vision, 2016.
DOI : 10.1109/TPAMI.2011.208

URL : http://arxiv.org/pdf/1512.00570

[. Yousfi, S. Berrani, and C. Garcia, Deep learning and recurrent connectionist-based approaches for Arabic text recognition in videos, 2015 13th International Conference on Document Analysis and Recognition (ICDAR), 2015.
DOI : 10.1109/ICDAR.2015.7333917

URL : https://hal.archives-ouvertes.fr/hal-01152209

R. Yeh, C. Chen, T. Y. Lim, M. Hasegawa-johnson, N. Minh et al., Semantic Image Inpainting with Perceptual and Contextual Losses, p.160707539, 2016.

D. Yi, Z. Lei, S. Liao, Z. Stan, and . Li, Learning face representation from scratch, CoRR abs, p.7923, 1411.

D. Yi, Z. Lei, Z. Stan, and . Li, Age Estimation by Multi-scale Convolutional Network, Proceedings of Asian Conference on Computer Vision. Singapore, 2014.
DOI : 10.1007/978-3-319-16811-1_10

A. Leslie and . Zebrowitz, Reading faces: Window to the soul?, 1997.

D. Matthew, R. Zeiler, and . Fergus, Visualizing and understanding convolutional networks, Proceedings of European Conference on Computer Vision, 2014.

W. Zhao, R. Chellappa, J. Phillips, and A. Rosenfeld, Face recognition, ACM Computing Surveys, vol.35, issue.4, pp.399-458, 2003.
DOI : 10.1145/954339.954342

URL : https://hal.archives-ouvertes.fr/hal-01704659

L. Zhang, R. Chu, S. Xiang, S. Liao, Z. Stan et al., Face Detection Based on Multi-Block LBP Representation, Proceedings of International Conference on Biometrics, 2007.
DOI : 10.1007/978-3-540-74549-5_2

[. Zhou, B. Georgescu, X. S. Zhou, and D. Comaniciu, Image based regression using boosting method, Proceedings of International Conference on Computer Vision, 2005.

B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva, Learning deep features for scene recognition using places database, Proceedings of advances in Neural Information Processing Systems, 2014.
DOI : 10.1109/tpami.2017.2723009

Y. Zhu, Y. Li, G. Mu, and G. Guo, A Study on Apparent Age Estimation, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW)
DOI : 10.1109/ICCVW.2015.43

[. Zhu, P. Krähenbühl, E. Shechtman, and A. A. Efros, Generative Visual Manipulation on the Natural Image Manifold, Proceedings of European Conference on Computer Vision, 2016.
DOI : 10.1109/CVPR.2013.299

URL : http://arxiv.org/pdf/1609.03552

[. Zhu, T. Park, P. Isola, and A. A. Efros, Unpaired image-toimage translation using cycle-consistent adversarial networks, Proceedings of International Conference on Computer Vision, 2017.
DOI : 10.1109/iccv.2017.244

[. Zhou, C. Paul, J. Miller, and . Zhang, Age classification using Radon transform and entropy based scaling SVM, Procedings of the British Machine Vision Conference 2011, 2011.
DOI : 10.5244/C.25.28

URL : http://www.bmva.org/bmvc/2011/proceedings/paper28/paper28.pdf

Z. Zhang, Y. Song, and H. Qi, Age Progression/Regression by Conditional Adversarial Autoencoder, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
DOI : 10.1109/CVPR.2017.463

URL : http://arxiv.org/pdf/1702.08423

Y. Zhang and D. Yeung, Multi-task warped Gaussian process for personalized age estimation, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010.
DOI : 10.1109/CVPR.2010.5539975

URL : http://www.cs.ust.hk/%7Edyyeung/paper/pdf/yeung.cvpr2010.pdf