SexNet: A neural network identifies sex from human faces
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Face and Gesture Recognition: Overview
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Mathematical Psychology
Boosted mixture of experts: an ensemble learning scheme
Neural Computation
Learning Gender with Support Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition through Geometrical Features
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Gender Recognition in Non Controlled Environments
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Journal of Cognitive Neuroscience
On diversity and accuracy of homogeneous and heterogeneous ensembles
International Journal of Hybrid Intelligent Systems
Integrating independent components and linear discriminant analysis for gender classification
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Combinations of weak classifiers
IEEE Transactions on Neural Networks
Competitive neural trees for pattern classification
IEEE Transactions on Neural Networks
A novel training weighted ensemble (TWE) with application to face recognition
Applied Soft Computing
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This paper presents a new approach for building a committee machine (LVQCM) that is based on learning vector quantization (LVQ) neural networks. The proposed committee machine was then applied to solve the problem of facial gender recognition. Design of individual classifiers is time consuming and results in inaccurate and unstable classifiers. Settling on the right design parameters of a classifier is a non-trivial task. To avoid the abovementioned problems, a committee machine is implemented. Experimental results based on Kuwait University and Stanford University face databases indicate that the performance of the proposed committee machine (99.02%) outperforms that of the best individual classifier used in that combination (93%). Majority voting is used for combining the individual decisions of a group of LVQ weak classifiers generated and trained under different conditions. The experimental results also show that LVQCM outperforms other recently published methods such as: the K-Means, 2$^{nd}$ weight, Mahalanobis, linear discriminant, local linear discriminant, closest match, and the closest diffusion match. The implemented algorithm is not restricted to LVQ neural network and could be applied to other tytpes of neural networks.