Facial Expressions Classification Using Learning Vector Quantization Networks
This paper, an automatic facial expression recognition system is developed based on Gabor wavelet methodology and learning vector quantization networks (LVQs). Facial attributes from the frontal images are extracted in the form of feature vectors by evaluation the responses from a set of 18 complex Gabor filters at 34 fiducial points on face. The resultant high dimensional feature vectors are condensed by performing PCA and are classified into classes of expression using LVQs. Since the groups of feature vectors are non-linear separable, LVQ networks are chosen for classification instead of multilayer perceptron neural networks. In this paper, the optimum number of sub-groups, learning rate and other features of LVQ are determined to maximize the recognition accuracy of the system, using a set of designed experiments.
N. K. Chennamsetty et al., "Facial Expressions Classification Using Learning Vector Quantization Networks," Intelligent Engineering Systems Through Artificial Neural Networks, (ANNIE '04), American Society of Mechanical Engineers (ASME), Jan 2004.
Mining and Nuclear Engineering
Electrical and Computer Engineering
Article - Conference proceedings
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