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Articles by Surapong Auwatanamongkol
Total Records ( 3 ) for Surapong Auwatanamongkol
  Mohammad Shahidul Islam and Surapong Auwatanamongkol
  The success of a good facial expression recognition system depends on the facial feature descriptor. This study presents a unique local facial feature descriptor, the Local Arc Pattern (LAP) for facial expression recognition. Feature is obtained from a local 5x5 pixels region by comparing the gray color intensity values surrounding the referenced pixel to formulate two separate binary patterns for the referenced pixel. Each face is divided into equal sized blocks and histograms of LAP codes from those blocks are concatenated to build the feature vector for classification. The recognition performance of proposed method was evaluated on popular Japanese Female facial expression dataset using support vector machine as the classifier. Extensive experimental results with prototype expressions show that proposed feature descriptor outperforms several popular existing appearance-based feature descriptors in terms of classification accuracy.
  Mohammad Shahidul Islam and Surapong Auwatanamongkol
  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.
  Mohammad Shahidul Islam and Surapong Auwatanamongkol
  The success of a good facial expression recognition system depends on the facial feature descriptor. Features extracted from local region are widely used for facial expression recognition due to their simplicity but the long feature vector length produces by them makes the overall system slow for recognition. This study presents a unique local facial feature descriptor, the Local Arc Pattern (LAP) for facial expression recognition. Feature is obtained from a local 5x5 pixels region by comparing the gray color intensity values surrounding the referenced pixel to formulate two separate binary patterns for the referenced pixel. Each face is divided into equal sized blocks and histograms of LAP codes from those blocks are concatenated to build the feature vector for classification. The recognition performance of proposed method was evaluated on popular Japanese Female Facial Expression dataset using Support Vector Machine as the classifier. Extensive experimental results with prototype expressions show that proposed feature descriptor outperforms several popular existing appearance-based feature descriptors in terms of classification accuracy.
 
 
 
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