1. Introduction

Due to the intelligence security monitoring is more popular in recent years, the automatically recognizing face is needed for various visual surveillance systems, for example the accessing control system for personal or company to verify the legal/illegal people, policing system for identifying the thief and the robber who presents the illegal behavior in public or private space. To construct an efficient face recognition system, the facial descriptor with discriminated characteristic is required.

© 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.

The facial descriptor refers to the process of extracting the discriminative features to represent a given face image. Numerous methodologies are proposed to recognize face and those can be classified as global and local facial descriptors. The global facial descriptor describes the facial characteristics with the whole face image, such as principal component analysis (PCA) [1, 2] and linear discriminant analysis (LDA) [3, 4]. PCA converts the global facial descriptor from high dimension to low dimension by using the linear transform methodology to reduce the computational cost. Linear discriminant analysis (LDA) also called the Fishers Linear Discriminant is similar to PCA, while it is a supervised methodology. Although the global facial descriptor can extract the principal component from the facial images, reduces the computational cost, and maintains the variance of the facial image, the performance is sensitive to the change of the environment, such as the change of light.

The flexibilities of the local facial descriptors are better than the global facial descriptors because they successfully and effectively represent the spatial structure information of an input image. A well local facial descriptor generates discriminative and robust features to achieve good recognition results with computational simplicity. In this chapter, we represent a number of approaches in the local facial descriptor including the local binary pattern (LBP), local derivation pattern (LDP), local tetra pattern (LTrP), local vector pattern (LVP) and local clustering pattern (LCP).
