Recognition of facial expressions using Gabor wavelets and learning vector quantization
Engineering Applications of Artificial Intelligence
A testing methodology for face recognition algorithms
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
Multimodal priority verification of face and speech using momentum back-propagation neural network
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
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In this paper, we present a scheme for face authentication in the presence of variations. To deal with variations, such as facial expressions and registration errors, with which traditional appearance-based methods do not perform well, we propose the eigenflow approach. In this approach, the optical flow and the optical flow residue between a test image and a training image are computed first. The optical flow is then fitted to a model that is pre-trained by applying principal component analysis (PCA) to optical flows resulting from variations caused by facial expressions and registration errors. The eigenflow residue, optimally combined with the optical flow residue using linear discriminant analysis (LDA), determines the authenticity of the test image. Experimental results show that the proposed scheme outperforms the traditional methods in the presence of expression variations and registration errors. The approach can be extended to model lighting and pose variations as well.