SexNet: A neural network identifies sex from human faces
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Pedestrian Detection Using Wavelet Templates
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Boosting Sex Identification Performance
International Journal of Computer Vision
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Object Recognition with Cortex-Like Mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Biologically Inspired Features for Face Processing
International Journal of Computer Vision
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Locality sensitive discriminant analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Orthogonal Laplacianfaces for Face Recognition
IEEE Transactions on Image Processing
Demographic classification with local binary patterns
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Recognizing human gender in computer vision: a survey
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
A convolutional neural network for pedestrian gender recognition
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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In this paper we study the problem of gender recognition from human body To represent human body images for the purpose of gender recognition, we propose to use the biologically-inspired features in combination with manifold learning techniques A framework is also proposed to deal with the body pose change or view difference in gender classification Various manifold learning techniques are applied to the bio-inspired features and evaluated to show their performance in different cases As a result, different manifold learning methods are used for different tasks, such as the body view classification and gender classification at different views Based on the new representation and classification framework, a gender recognition accuracy of about 80% can be obtained on a public available pedestrian database.