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Journal of Applied Sciences
Year: 2013  |  Volume: 13  |  Issue: 6  |  Page No.: 837 - 845

Gradient Direction Pattern: A Gray-scale Invariant Uniform Local Feature Representation for Facial Expression Recognition

Mohammad Shahidul Islam and Surapong Auwatanamongkol    

Abstract: Local feature representations are widely used for facial expression recognition due to their simplicity and high accuracy rates achieved. However, local feature representations usually produce a long feature vector to represent a facial image and hence, require long processing time for training and recognition. To alleviate this problem, a simple gray-scale invariant local feature representation is proposed for facial expression recognition. The proposed local feature pattern at a pixel level, represented by a four-bit pattern, is derived based on the gradient directions of the gray color values of its neighboring pixels. A histogram of sixteen bins is required to count numbers of the patterns at the pixel level in a block. The histograms of all blocks in an image are concatenated to form the final local feature vector. To reduce the length of the local feature vector, a variance based feature selection method is used to select patterns that are more relevant and eight out of the sixteen possible patterns can be discarded without compromising the recognition rates. In addition, the result patterns become uniform. Experiments were performed on extended Cohn-Kanade and Japanese JAFFE datasets using Support Vector Machines as classifiers. The experimental results do show that the proposed feature representation is more effective than other local feature representations in terms of accuracy rates and processing time.

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