Faceprint: Fusion of local features for 3D face recognition

Abstract

3D face recognition is a very active biometric research field. Due to the 3D data's insensitivity to illumination and pose variations, 3D face recognition has the potential to perform better than 2D face recognition. In this paper, we focus on local feature based 3D face recognition, and propose a novel Faceprint method. SIFT features are extracted from texture and range images and matched, the matching number of key points together with geodesic distance ratios between models are used as three kinds of matching scores, likelihood ratio based score level fusion is conducted to calculate the final matching score. Thanks to the robustness of SIFT, shape index, and geodesic distance against various changes of geometric transformation, illumination, pose and expression, the Faceprint method is inherently insensitive to these variations. Experimental results indicate that Faceprint method achieves consistently high performance comparing with commonly used SIFT on texture images. © Springer-Verlag Berlin Heidelberg 2009.