4. Summary

of LTrP in the four quadrants of the rectangular (or Cartesian) coordinate system are altered

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

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

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 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 includ-

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

Methods Information used Distribution of coding scheme Feature Length

LBP Original values One dimensional 8 LDP High-order derivative values One dimensional 8 � 4 LTrP High-order derivative values Two dimensional 8 � 13 LVP High-order derivative values Two dimensional 8 � 4 LCP High-order derivative values Two dimensional 8 � 4

, its feature length is 8 � 4 ¼ 32 bits, in which "8" is the number of

when illumination is changed.

108 From Natural to Artificial Intelligence - Algorithms and Applications

each method is demonstrated in Table 1.

clustering process of LCP.

ing 0<sup>∘</sup>

, 45<sup>∘</sup> , 90<sup>∘</sup>

the pairwise combinatorial directions.

, and 135<sup>∘</sup>

Table 1. Comparison of various methods.

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 field.
