Employing vector quantization on detected facial parts for face recognition

Abstract

Facial Parts Detection (FPD) approach in conjunction with Vector Quantization (VQ) algorithm are proposed in this paper for face recognition. There are three phases in the proposed system, namely, Preprocessing, Feature Extraction, and Classification. Detecting facial parts, which are nose, both eyes, and mouth, and choosing appropriate dimensions for each part are done in the preprocessing phase. In the feature extraction phase, four groups for each person, one group for each detected part, are constructed for dimensionality reduction and feature discrimination by considering all parts of all training poses. For further data compaction, VQ algorithm employing Kekre Fast Codebook Generation (KFCG) approach for codebook initialization is applied to each of the four groups. Finally, Euclidean distance criterion is used to obtain the recognition rates. Four databases, namely, ORL, YALE, FERET, and FEI that have different facial variations, such as illuminations, rotations, makeups, facial expressions, etc. are used to evaluate the proposed system. Experimental work is performed to evaluate the performance of the proposed technique and the state-of-the-arts approaches. Then, K-Fold Cross Validation (CV) is used to analyze the results. The proposed system consistently improved the recognition rates as well as the storage requirements. Sample results are given.