Improving Performance of Texture Based Face Recognition Systems by Segmenting Face Region

Textures play an important role in recognition of images. This paper investigates the efficiency of performance of three texture based feature extraction methods for face recognition. The methods for comparative study are Grey Level Co_occurence Matrix (GLCM), Local Binary Pattern (LBP) and Elliptical Local Binary Template (ELBT). Experiments were conducted on a facial expression database, Japanese Female Facial Expression (JAFFE). With all facial expressions LBP with 16 vicinity pixels is found to be a better face recognition method among the tested methods. Experimental results show that classification based on segmenting face region improves recognition accuracy. Keywords— face recognition, face anthropometric measures, grey level co_occurence matrix, local binary pattern, elliptical local binary template, chi-square statistic.

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