Local Clustering Patterns in Polar Coordinate for Face Recognition
Facial recognition is an important issue and has various practical applications in visual surveillance system. In this paper, we propose a novel local pattern descriptor called the Local Clustering Pattern (LCP) with low computational cost operating in the polar coordinate system for recognizing face. The local derivative variations with multi-direction are considered and that are integrated on 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 distance (r) and angle (\(\theta \)). LCP is ensemble of several decisions from the clustering algorithm for each pixel in the polar coordinate system (P.C.S.). Differs from the existing local pattern descriptors, such as local binary pattern (LBP) [1, 8], local derivation pattern (LDP) , and local tetra pattern (LTrP) , 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 performance of the proposed method is compared with LBP, LDP, LTrP on the Extended Yale B [4, 5] and CAS-PEAL  databases.
KeywordsLocal pattern descriptors Local clustering pattern (LCP) Rectangular coordinate system (R.C.S.) Polar coordinate system (P.C.S.)