Abstract—This paper presents novel modular kernel Eigen spaces approach to implement on the phase congruency images. Smaller sub-regions from a predefined neighbourhood within the phase congruency images of the training samples are merged to obtain a large set of features. These features are then projected into higher dimensional spaces using kernel methods. The proposed localized nonlinear feature selection procedure helps to overcome the bottlenecks of illumination variations, partial occlusions, expression variations and variations due to temperature changes that affect the face recognition techniques. Databases are used for experimentation and evaluation of the proposed technique. Also, a decision level methodology is presented which along with the feature selection procedure has outperformed various other face recognition techniques in terms of recognition accuracy.

Index Terms—Feature extraction, Kernel methods, Phase congruency

Cite: N Durga Rao, Sk ThatherBasha, P Balakrishna and D Bullibabu, "FACE RECOGNITION BY PHASE CONGRUENCY MODULAR KERNEL PRINCIPAL COMPONENT ANALYSIS," International Journal of Electrical and Electronic Engineering & Telecommunications, Vol. 6, No. 2, pp. 30-36, April 2017.