Combining texture with geometry for performance enhancement of facial recognition techniques

Texture features alone cannot help us to recognize faces, because there may be several people with similar texture features. Likewise geometry based features can also be similar in different people. When the two methods are experimented separately, there may be inaccurate results. There might be better results when the two methods are combined and used. So this paper tries to evaluate the performance of both the features independently and jointly in face recognition. Texture feature extraction methods such as Grey Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Elliptical Local Binary Template (ELBT) are proposed to combine with geometry based method. Japanese Female Facial Expression (JAFFE) database is used to conduct the test. Experiments are carried out in two ways. First, all the methods are experimented with images having less expression. Then they are tested with all images in the database. Experimental results show that ELBT outperform the other methods and also the recognition rate is higher when texture features are combined with geometry based features. For classification K-nearest neighbourhood algorithm is used. And for recognition Chi square statistic (χ 2) is used as dissimilarity measure.