Latent variable models and factors analysis
Latent variable models and factors analysis
Self-organizing maps
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
GTM: the generative topographic mapping
Neural Computation
Learning Gender with Support Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward Automatic Simulation of Aging Effects on Face Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Human Recognition from Video
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Face Recognition from Video: A CONDENSATION Approach
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Boosting Sex Identification Performance
International Journal of Computer Vision
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Automatic Age Estimation Based on Facial Aging Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
An experimental comparison of gender classification methods
Pattern Recognition Letters
A pose-wise linear illumination manifold model for face recognition using video
Computer Vision and Image Understanding
Manifold Learning for Gender Classification from Face Sequences
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Learning personal specific facial dynamics for face recognition from videos
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Age Synthesis and Estimation via Faces: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video-based face recognition using probabilistic appearance manifolds
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Video-based face recognition using adaptive hidden markov models
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Ensemble of global and local features for face age estimation
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Demographic classification with local binary patterns
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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Research on automatic demographic classification is still in its infancy despite the vast potential applications. The few existing works are only based on static images while nowadays input data in many real-world applications consist of video sequences. From these observations and also inspired by studies in neuroscience emphasizing manifold ways of visual perception, we propose in this work a novel approach to demographic classification from video sequences which encodes and exploits the correlation between the face images through manifold learning. Our extensive experiments on the gender and age classification problems show that the proposed manifold learning based approach yields in excellent results outperforming those of traditional static image based methods. Furthermore, to gain insight into the proposed approach, we also investigate an LBP (local binary patterns) based spatiotemporal method as a baseline system for combining spatial and temporal information to demographic classification from videos.