Gender shades: Intersectional accuracy disparities in commercial gender classification, Conference on Fairness, pp.77-91, 2018. ,
A survey on face detection in the wild: past, present and future, Computer Vision and Image Understanding, vol.138, pp.1-24, 2015. ,
What else does your biometric data reveal? a survey on soft biometrics, IEEE Transactions on Information Forensics and Security, pp.1-26, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01247885
An all-in-one convolutional neural network for face analysis, Automatic Face & Gesture Recognition (FG 2017, pp.17-24, 2017. ,
Dager: Deep age, gender and emotion recognition using convolutional neural network, 2017. ,
Deep neural networks are more accurate than humans at detecting sexual orientation from facial images, Journal of personality and social psychology, vol.114, issue.2, p.246, 2018. ,
, Automated inference on criminality using face images, pp.4038-4052, 2016.
Show me your face and i will tell you your height, weight and body mass index, International Coference on Pattern Recognition (ICPR), 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01799574
Female facial aesthetics based on soft biometrics and photo-quality, IEEE International Conference on Multimedia and Expo (ICME), 2011. ,
Face recognition performance: Role of demographic information, IEEE Transactions on Information Forensics and Security, vol.7, issue.6, pp.1789-1801, 2012. ,
Face recognition vendor test (FRVT) performance of automated gender classification algorithms. US Department of Commerce, 2015. ,
Demographic classification: Do gender and ethnicity affect each other?, Informatics, Electronics & Vision (ICIEV), 2012 International Conference on, pp.383-390, 2012. ,
Fairness through awareness, Proceedings of the 3rd innovations in theoretical computer science conference, pp.214-226, 2012. ,
The perpetual line-up: Unregulated police face recognition in america, 2016. ,
Classifying adults' and children's faces by sex: Computational investigations of subcategorical feature encoding, Cognitive science, vol.25, issue.5, pp.819-838, 2001. ,
Gender estimation based on smile-dynamics, IEEE Transactions on Information Forensics and Security, vol.12, issue.3, pp.719-729, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01412408
Can a smile reveal your gender?, 2016 International Conference of the, pp.1-6, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01387134
, L2-constrained softmax loss for discriminative face verification, 2017.
Inclusivefacenet: Improving face attribute detection with race and gender diversity, Workshop on Fairness, Accountability, and Transparency in Machine Learning (FAT/ML), 2018. ,
Decoupled classifiers for group-fair and efficient machine learning, Conference on Fairness, pp.119-133, 2018. ,
Age, gender and race estimation from unconstrained face images, Dept. Comput. Sci. Eng, 2014. ,
Pushing the frontiers of unconstrained face detection and recognition: Iarpa janus benchmark a, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.1931-1939, 2015. ,
Age and gender classification using convolutional neural networks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp.34-42, 2015. ,
DOI : 10.1109/cvprw.2015.7301352
Chalearn looking at people and faces of the world: Face analysis workshop and challenge, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp.1-8, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01381152
Age progression / regression by conditional adversarial autoencoder, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. ,
DOI : 10.1109/cvpr.2017.463
URL : http://arxiv.org/pdf/1702.08423
Facenet: A unified embedding for face recognition and clustering, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.815-823, 2015. ,
DOI : 10.1109/cvpr.2015.7298682
URL : http://arxiv.org/pdf/1503.03832
, Learning face representation from scratch, 2014.
Dynamic multi-task learning with convolutional neural network ,
DOI : 10.24963/ijcai.2017/231
URL : https://www.ijcai.org/proceedings/2017/0231.pdf
Multi-task convolutional neural network for pose-invariant face recognition, IEEE Transactions on Image Processing, 2017. ,
DOI : 10.1109/tip.2017.2765830
URL : http://arxiv.org/pdf/1702.04710
Labeled faces in the wild: A database forstudying face recognition in unconstrained environments, Workshop on faces in'Real-Life'Images: detection, alignment, and recognition, 2008. ,
URL : https://hal.archives-ouvertes.fr/inria-00321923
Ms-celeb-1m: A dataset and benchmark for large-scale face recognition, European Conference on Computer Vision, pp.87-102, 2016. ,
DOI : 10.1007/978-3-319-46487-9_6
URL : http://arxiv.org/pdf/1607.08221
Inception-v4, inception-resnet and the impact of residual connections on learning, 2017. ,
Joint face detection and alignment using multitask cascaded convolutional networks, IEEE Signal Processing Letters, vol.23, issue.10, pp.1499-1503, 2016. ,
DOI : 10.1109/lsp.2016.2603342
URL : http://arxiv.org/pdf/1604.02878