3. Comparison

In this section, we discuss the characteristics of the local patterns descriptors as mentioned. The local binary pattern (LBP) generates the local facial descriptor by comparing the gray value between referenced pixel and its adjacent pixels for each pixel in the face image. The texture information, such as spots, lines and corners, in the images is extracted. Although LBP considers the spatial information to generate the local facial descriptor, it omits the directional information and is sensitivity when light is slightly changed.

The local derivation pattern (LDP) analyzes the turnings between referenced pixel and its neighborhoods from the derivative values. The derivative values with four directions are considered to generate the local facial descriptor in the high-order derivative space. However, the turnings between referenced pixel and its neighbors are discussed in the same derivative direction.

The local tetra pattern (LTrP) utilized the two-dimensional distribution with derivative values in four quadrants to describe the texture information and that can extract more discriminative information. Although LTrP considers the derivative variations with two dimensions, there exist two problems: (1) the dimension of facial descriptor and (2) the sensitivity of the features. To compare with LBP and LDP, the dimension of facial descriptor of LTrP is high. The features of LTrP in the four quadrants of the rectangular (or Cartesian) coordinate system are altered when illumination is changed.

of referenced pixel, "3" is the number of the binary patterns in a tetra pattern, "4" is the number of the tetra patterns, and "1" number of the binary pattern which is obtained from the magnitude; the feature length of LVP and LCP is 8 � 4 ¼ 32 bits, where "8" is the number of neighborhood of referenced pixel, and "4" is the number of pairwise combinatorial

Local Patterns for Face Recognition

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http://dx.doi.org/10.5772/intechopen.76571

The principal object of this chapter is to present the local pattern descriptors for understanding and accessing the facial descriptor in face recognition. The concept of local pattern is sample and intuitive, and the extended techniques of the basic local pattern are widely used in various areas. A partial listing of local pattern descriptors includes local binary pattern (LBP), local derivative pattern (LDP), local tetra patterns (LTrP), local vector pattern (LVP) and local clustering pattern (LCP) are widely applied to variety of image processing problems such as object detection, object recognition, image retrieval, fingerprint recognition, character recognition, face recognition, license plate recognition. Since it is impractical to cover all the approaches of local pattern descriptor in a single chapter, the basic and popular techniques included are chosen for their value in introducing and clarifying fundamental concepts in the

College of Computer and Information Sciences, Fujian Agriculture and Forestry University,

[1] Moghaddam B, Pentland A. Probabilistic visual learning for object representation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1997;19(7):696-710. DOI: 10.11

[2] Turk M, Pentland A. Eigenfaces for recognition. Journal of Cognitive Neuroscience. 1991;

[3] Belhumeur PN, Hespanha JP, Kriegman DJ. Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intel-

directions.

field.

Author details

Address all correspondence to: cwlin@fafu.edu.cn

3(1):71-86. DOI: 10.1162/jocn.1991.3.1.71

ligence. 1997;19(7):711-720. DOI: 10.1109/34.598228

Chih-Wei Lin

Fuzhou, China

References

09/34.598227

4. Summary

The local vector pattern (LVP) designs the comparative space transform (CST) and that is associated with the pairwise directions of vector to encode the micropatterns. Comparing LVP with LBP, LDP, and LTrP, LVP not only successfully extracts distinctive information but also reduces the feature length. However, its computational cost is higher than LBP and LDP.

The local clustering pattern (LCP) derivatives the local variations with multidirections and that are integrated to form the pairwise combinatorial direction. To generate the discriminative local pattern, the features of local derivative variations are transformed into the polar coordinate system by generating the characteristics of magnitude (m) and orientation (θ). LCP generates the discriminative local clustering pattern with low-order derivative space and low computational cost which are stable in the process of face recognition. The summarization of each method is demonstrated in Table 1.

In Table 1, we analyze these methods with three indicators: (1) information used, (2) distribution of coding scheme, and (3) feature length. The indicator of the information used presents the information which is used in facial descriptor generation. LBP uses the original values such, as gray value; LDP considers the single high-order derivative values; LTrP uses both horizontal and vertical high-order derivative values; LVP uses the high-order derivative values and be described as the vector representation; the high-order derivative values are utilized in clustering process of LCP.

The distribution of coding scheme is to present how many directions of used information are considered in coding at each time. LBP and LDP generate the micropattern by considering a single direction at each time, for example, LDP generates the micropatterns of one direction at a time and then integrates the results of each direction to form the facial descriptor; LTrP considers two-direction information, horizontal and vertical, when coding; LVP and LCP use the pairwise combinatorial directions.

The feature length is to demonstrate the feature length of each micropattern. LBP considers eight neighborhoods and its feature length is 8; LDP further considers four directions including 0<sup>∘</sup> , 45<sup>∘</sup> , 90<sup>∘</sup> , and 135<sup>∘</sup> , its feature length is 8 � 4 ¼ 32 bits, in which "8" is the number of neighborhood of referenced pixel and "4" is the number of derivative directions; the feature length of LTrP is 8 � 13 ¼ 8 � ð Þ¼ 3 � 4 þ 1 104 bits, where "8" is the number of neighborhood


Table 1. Comparison of various methods.

of referenced pixel, "3" is the number of the binary patterns in a tetra pattern, "4" is the number of the tetra patterns, and "1" number of the binary pattern which is obtained from the magnitude; the feature length of LVP and LCP is 8 � 4 ¼ 32 bits, where "8" is the number of neighborhood of referenced pixel, and "4" is the number of pairwise combinatorial directions.
