Instance-Based Learning Algorithms
Machine Learning
EMPATH: face, emotion, and gender recognition using holons
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
SexNet: A neural network identifies sex from human faces
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Machine Learning
Learning Gender with Support Faces
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
Overview of the Face Recognition Grand Challenge
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A review on Gabor wavelets for face recognition
Pattern Analysis & Applications
Gender Recognition in Non Controlled Environments
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Near-duplicate keyframe retrieval by nonrigid image matching
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Aggregation Pheromone Density Based Pattern Classification
Fundamenta Informaticae
Gender Classification Using Interlaced Derivative Patterns
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Multi-view gender classification using local binary patterns and support vector machines
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Learning local binary patterns for gender classification on real-world face images
Pattern Recognition Letters
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Gender categorization, based on the analysis of facial appearance, can be useful in a large set of applications. In this paper we investigate the gender classification problem from a non-conventional perspective. In particular, the analysis will aim to determine the factors critically affecting the accuracy of available technologies, better explaining differences between face-based identification and gender categorization. A novel challenging protocol is proposed, exploiting the dimensions of the Face Recognition Grand Challenge version 2.0 database (FRGC2.0). This protocol is evaluated against several classification algorithms and different kind of features, such as Gabor and LBP. The results obtained show that gender classification can be made independent from other appearance-based factors such as the skin color, facial expression, and illumination condition.