Section 3 Face Recognition

Chapter 6

Abstract

1. Introduction

113

Face Recognition

possible applications are presented in this chapter.

transform, histogram of oriented gradients, local binary patterns

Spatial Domain Representation for

Toshanlal Meenpal, Aarti Goyal and Moumita Mukherjee

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

Keywords: spatial domain representation, face recognition, scale-invariant feature

Face recognition is a powerful biometric system in today's highly technological world. It is widely accepted over other biometric systems like, finger print, iris or speech recognition for security, surveillance, and commercial applications. Face recognition system is generally a procedure of multiple major stages: face detection, preprocessing, feature extraction and verification. A complete structure of face recognition system is shown in Figure 1. Face detection detects a single face or number of faces present in a given image. Viola-Jones face detection algorithms using Haar features [1], faster R-CNN face detector [2], and face detection based on Histograms of Oriented Gradient [3] are popular methods for detecting faces in an image. Generally, images are captured under unconstrained environment and hence needed to be preprocessed before feeding to feature extraction stage. Preprocessing mainly aims to reduce noise effect, difference of illumination, color intensity, background, and orientation. The correct recognition of image depends upon quality of captured image, lighting condition etc. [4]. Recognition rate can be improved by performing pre-processing on the captured image. Various pre-processing

## Chapter 6
