1. Introduction

Nowadays, face recognition is a non-intrusive biometric method in which the data acquisition is easy and can be carried out with or without the cooperation of the person under analysis. The face can be considered as the easiest way to recognize a person, increasing the acceptance of this kind of systems and their applications [1–3]. These systems consist of two tasks: identity verification, where the system verifies if the identity of the person is that which he/she claims it to be, and identification task, where the system determines the identity of the person among all the people in a database. Thus, the recognition task covers both tasks–identification and verification [4, 5].

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Several problems must be considered in the development of a face recognition system such as illumination changes, facial expressions, and partial occlusions. It is because these kinds of changes can harm the accuracy of a face recognition system [6]. The changes in lighting conditions have received significant attention [6]. Because of that, a lot of systems were proposed in the last years, trying to reduce these problems [6]. Some systems proposed to this end are based on image processing techniques such as histogram equalization [6, 7] and contrast-limited adaptive histogram equalization (CLAHE) [8]. Another way to solve the problem of illumination changes is the development of different high-performance methods to solve these kinds of changes such as the eigenphases approach [7–11]. Also, are useful some methods based on frequency transforms like discrete cosine transform [12–14], discrete Gabor transform [15–17], discrete wavelet transforms [18–21], and discrete Haar transform [22]. Additional methods that could be applied are the eigenfaces [23, 24] which use the principal component analysis (PCA) [25, 26], modular PCA-based face recognition methods [27], the Fisherfaces approach [28], and the Laplacianfaces [29].

2.1. Texture descriptors

scheme.

label of each pixel is multiplied by 2<sup>p</sup>

In this chapter were used two texture descriptors, the hLBPI and the WBP. The hLBPI algorithm, introduced by Ojala et al. [34], is based on the original LBP method. This algorithm uses masks of 3 3 pixels. There is a neighborhood, as is shown in Figure 2a, where all neighbors are compared with the central pixel where each of these pixels are labeled with a 0 if their values are smaller than the central pixel; otherwise, they are labeled as 1 (Figure 2b). Next, the

from 0 to 7 (Figure 2c). Finally, all values are added to get the label that will be positioned in the place of the central pixel as shown in Figure 2d. This algorithm obtains 128 different values

Figure 1. (a) Block diagram of the evaluated face recognition scheme, (b) illustration of the evaluated face recognition

for the central pixel. These steps apply to an image to obtain a LBP matrix.

, where p is the position of each pixel in the neighborhood

Face Recognition Based on Texture Descriptors http://dx.doi.org/10.5772/intechopen.76722 113

The local binary pattern (LBP) operator [30] has recently been proposed in several applications. The principal advantages of this algorithm are that it has a good computational performance and presents a good support when the images have gray-level changes. Because of that LBP can be applied for image characterization in several pattern recognition tasks [31]. This algorithm can be used for face characterization because the face images have a lot of little patterns which can be characterized using the LBP [30]. Several LBP variations have been proposed such as: the holistic LBP histogram (hLBPH) [30], the spatially enhanced LBP histogram (eLBPH) [32], holistic LBP Image algorithm (hLBPI) [32] and decimated image window binary pattern (WBP) [33]. All of these algorithms are based on the original LBP algorithm, but the computational complexity of the hLBPI and WBP are lower than the others providing also a good performance as shown in this chapter.

In recent years, the interest in the face recognition schemes has increased because of its potential implementation in mobile devices, which generally have a limited computational power. Hence, this chapter presents a comparison of the texture descriptors like hLBPI and WBP. Finally, some classification methods, like SVM, Euclidean distance, and cosine distance, are used to perform the recognition. In this chapter, these algorithms were evaluated with different illumination and facial expression changes.

The remainder of this chapter is organized as follows: Section 2 presents the description of the evaluated system. Section 3 presents the evaluation results. Finally, Section 4 provides the conclusions of this research.
