Descriptor feature based on local binary pattern for face classification

Abstract

Local Binary Patterns (LBP) is a non-parametric descriptor whose purpose is to effectively summarize local image configurations. It has generated increasing interest in many aspects including facial image analysis, vision detection, facial expression analysis, demographic classification, etc. in recent years and has proven useful in various applications. This paper presents a local binary pattern based face recognition (LBP) technology using a Vector Support Machine (SVM). Combine the local characteristics of LBP with universal characteristics so that the general picture characteristics are more robust. To reduce dimension and maximize discrimination, super vector machines (SVM) are used. Screened and Evaluated (FAR), FARR and Accuracy Score (Acc), not only on the Yale Face database but also on the expanded Yale Face Database B datasets, the test results indicate that the approach is accurate and practical, and gives a recognition rate of 98 %.