A Novel Illumination Invariant Face Recognition Method Based on PCA and WPD Using YCbCr Color Space

Today, much research on face recognition has focused on using grey-scale images. With the increasing availability of color images, it makes sense to develop approaches for integrating color information into recognition process as the grey-scale approaches is sensitive to lighting variations. In this paper, we have proposed a novel two phase method, i.e., YCbCr-WPD-PCA-Mah. In the first phase, we convert the each training face into Y, Cb and Cr components and then decompose Y, Cb and Cr components into k parts using the Wavelet Packet Decomposition(WPD). finally perform PCA for k-times on Y, Cb and Cr subbands, to get k eigenspaces and k feature vectors for each Y, Cb and Cr subbands. In the second phase i.e., classification phase, the test image is projected onto the Y-eigenspace, Cb-eigenspace, and Cr-eigenspace after being decomposed into k part using WPD. Then, the Mahalanobis Distance is computed between the test image and all the training images in Y-subspace, Cb-subspace, and Cr-subspace. The Mahalanobis Distance is computed between the merged feature vectors. In deceision level, we compute the mean of the Mahalanobis Distances obtained from Y, Cb and Cr subspaces. The face has the best match with the test image is which has the minimum distance. The accuracy of the proposed method i.e., YCbCr-WPD-PCA-Mah has been identified and a comparison was performed in terms of recognition rates or Equal Error Rate(EER) or the Receiver Operating Curves(ROC).