Learning Gender with Support Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Unified Learning Framework for Real Time Face Detection and Classification
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
On Probabilistic Combination of Face and Gait Cues for Identification
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Gait Analysis for Recognition and Classification
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Integrating Face and Gait for Human Recognition
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Gender identification using a general audio classifier
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Language independent gender identification
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 02
Gender Recognition using Adaboosted Feature
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 02
Learning gender from human gaits and faces
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
Gender classification in human gait using support vector machine
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
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In this paper, we consider the problem of gender recognition based on face and multi-view gait cues in the same walking sequence. The gait cues are derived from multiple simultaneous camera views. Meanwhile, the face cues are captured by a camera at front view. According to this setup, we build a database including 32 male subjects and 28 female subjects. Then, for face, we normalize the frame images decomposed from videos and introduce PCA to reduce image dimension. For gait, we extract silhouettes from videos and employ an improved spatio-temporal representation on the silhouettes to obtain gait features. SVM is then used to classify gender with face features and gait features from each view respectively. We employ three fusion approaches involving voting rule, weighted voting rule and Bayes combination rule at the decision level. The effectiveness of various approaches is evaluated on our database. The experimental results of integrating face and multi-view gait show an obvious improvement on the accuracy of gender recognition.