Entire Gabor kernel locality preserving Fisher discriminant analysis: Subspace approach for expression recognition

The important objective of this work is to utilization of entire Gabor features by enhancing the phase part of the Gabor and maximizing the Fishers ratio in nonlinear domain space by preserving the local information. Entire Gabor kernel locality preserving Fisher discriminant analysis (EGKLPFDA) approach is proposed. Both Gabor magnitude and spatially enhanced phase congruency parts are separately used for feature extraction. These two vector feature space is projected into KLPFDA subspace method by preserving the kernel discriminant locality structure of data. Projected subspace is normalized by Z-score normalization. Both normalized scores are fused by maximum fusion rule. Final score obtained from train and test image sets are used to distance matching using Euclidean distance algorithm and support vector machine (SVM) classifier is implemented to classify the expressions. Performance analysis is carried out by comparing earlier approaches. Experimental results on JAFFE, Yale, and FD database demonstrate the effectiveness of the proposed approach.