An Empirical Evaluation of the Local Texture Description Framework-Based Modified Local Directional Number Pattern with Various Classifiers for Face Recognition

Texture is one of the chief characteristics of an image. In recent years, local texture descriptors have garnered attention among researchers in describing effective texture patterns to demarcate facial images. A feature descriptor titled Local Texture Description Framework-based Modified Local Directional Number pattern (LTDF_MLDN), capable of encoding texture patterns with pixels that lie at dissimilar regions, has been proposed recently to describe effective features for face images. However, the role of the descriptor can differ with different classifiers and distance metrics for diverse issues in face recognition. Hence, in this paper, an extensive evaluation of the LTDF_MLDN is carried out with an Extreme Learning Machine (ELM), a Support Vector Machine (SVM) and a Nearest Neighborhood Classifier (NNC) which uses Euclidian, Manhattan, Minkowski, G-statistics and chisquare dissimilarity metrics to illustrate differences in performance with respect to assorted issues in face recognition using six benchmark databases. Experimental results depict that the proposed descriptor is best suited with NNC for general case and expression variation, whereas, for the other facial variations ELM is found to produce better results.