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Automatic Face recognition system struggles to recognize face images acquired at varying illumination conditions, facial expression, aging, and pose. The focus of this research work is to enhance the illumination affected face images, which subsequently results in improved face recognition accuracy. This paper presents a new contrast enhancement technique for face images. It is a hybrid contrast enhancement technique based on the combinatorial approach of Completely Overlapped Uniformly Decrementing Sub-block Histogram Equalization (COUDSHE) with various neighbourhood metric. COUDSHE concentrated to bring out a unique framework for localizing a global technique to adapt efficiently to local lightening conditions. The idea of using neighbourhood metric with histogram equalization results in enhancing the contrast of the image. Hence, the combination of COUDSHE with neighbourhood metrics results in enhanced images that are both adaptive to local brightness and good contrast enhancement. The application of our hybrid technique on the Extended Yale B Database has proved to improve the contrast of images and as a result, there is significant improvement in the recognition accuracy of the face recognition system implemented using Principal Component Analysis.

A Hybrid Face Image Contrast Enhancement Technique for Improved Face Recognition Accuracy