Palmprint and Face Biometrics for Person Authentication Using Color Images

Multimodal biometric systems aim to improve the recognition accuracy by minimizing the limitations of unimodal systems. There exist many unimodal biometric traits used for person authentication such as face, iris, fingerprint, palmprint, voice, etc. In this thesis, a study on face and palmprint authentication using color images to construct an effective multimodal system is presented. Face recognition has been studied extensively in the biometrics community. Color face recognition approaches also exist in the literature in the last decades. On the other hand, automated palmprint identification using palmprint images has been extensively studied in the literature. However, most of the palmprint identification approaches exploit the gray-level images and there has been very little efforts to improve the palmprint identification using color information. Therefore, the use of color information from palmprint and face images using RGB, YCbCr and HSV color space representations are studied in this thesis. The experiments are conducted on publicly available color face and color palmprint databases. The results are presented on RGB, YCbCr and HSV color spaces. The effect of different color spaces on face and palmprint authentication is presented at the end of the thesis. Keywords: Multimodal biometric systems, unimodal biometric traits, face biometrics, palmprint biometrics, color spaces, gray-level images.