Spatial Domain Representation for Face Recognition

Abstract

Spatial domain representation for face recognition characterizes extracted spatial facial features for face recognition. This chapter provides a complete understanding of well-known and some recently explored spatial domain representations for face recognition. Over last two decades, scale-invariant feature transform (SIFT), histogram of oriented gradients (HOG) and local binary patterns (LBP) have emerged as promising spatial feature extraction techniques for face recognition. SIFT and HOG are effective techniques for face recognition dealing with different scales, rotation, and illumination. LBP is texture based analysis effective for extracting texture information of face. Other relevant spatial domain representations are spatial pyramid learning (SPLE), linear phase quantization (LPQ), variants of LBP such as improved local binary pattern (ILBP), compound local binary pattern (CLBP), local ternary pattern (LTP), three-patch local binary patterns (TPLBP), four-patch local binary patterns (FPLBP). These representations are improved versions of SIFT and LBP and have improved results for face recognition. A detailed analysis of these methods, basic results for face recognition and possible applications are presented in this chapter.