An Illumination Invariant Face Recognition Using 2D Discrete Cosine Transform and Clahe

Automatic face recognition performance is affected due to the head rotations and tilt, lighting intensity and angle, facial expressions, aging and partial occlusion of face using Hats, scarves, glasses etc. In this paper, illumination normalization of face images is done by combining 2D Discrete Cosine Transform and Contrast Limited Adaptive Histogram Equalization. The proposed method selects certain percentage of DCT coefficients and rest is set to 0. Then, inverse DCT is applied which is followed by logarithm transform and CLAHE. Thesesteps create illumination invariant face image, termed as 'DCT CLAHE' image. The fisher face subspace method extracts features from 'DCT CLAHE' imageand features are matched with cosine similarity. The proposed method is tested in AR database and performance measures like recognition rate, Verification rate at 1% FAR and Equal Error Rate are computed. The experimental results shows high recognition rate in AR database.