Regularized Independent Component Analysis in Face Verification

A regularized Independent Component Analysis (denoted as RICA) is proposed in the application of face verification. In RICA, information of correlation coefficients between images is employed to form a Laplacian matrix. This Laplacian matrix is used for locating localized features through regularizing the facial data before independent component analysis (ICA) feature extraction. Since there are two different architectures of ICA (ICA I and ICA II), RICA is implemented on these two architectures, namely RICA_ICA I and RICA_ICA II, respectively. Two face datasets are adopted to access the effectiveness of the proposed techniques. The databases are Facial Recognition Technology (FERET) and CMU Pose, Illumination, and Expression (CMU PIE). From the experimental results, it is demonstrated that the both proposed techniques, RICA_ICA I and RICA_ICA II, are able to show its superiority in face verification.