This paper presents a novel automatic framework to perform 3D face recognition. The proposed method uses a Simulated Annealing-based approach (SA) for range image registration with the Surface Interpenetration Measure (SIM), as similarity measure, in order to match two face images. The authentication score is obtained by combining the SIM values corresponding to the matching of four different face regions: circular and elliptical areas around the nose, forehead, and the entire face region. Then, a modified SA approach is proposed taking advantage of invariant face regions to better handle facial expressions. Comprehensive experiments were performed on the FRGC v2 database, the largest available database of 3D face images composed of 4,007 images with different facial expressions. The experiments simulated both verification and identification systems and the results compared to those reported by state-of-the-art works. By using all of the images in the database, a verification rate of 96.5 percent was achieved at a False Acceptance Rate (FAR) of 0.1 percent. In the identification scenario, a rank-one accuracy of 98.4 percent was achieved. To the best of our knowledge, this is the highest rank-one score ever achieved for the FRGC v2 database when compared to results published in the literature.

We present a methodology for face segmentation and facial landmark detection in range images. Our goal was to develop an automatic process to be embedded in a face recognition system using only depth information as input. To this end, our segmentation approach combines edge detection, region clustering, and shape analysis to extract the face region, and our landmark detection approach combines surface curvature information and depth relief curves to find the nose and eye landmarks. The experiments were performed using the two available versions of the Face Recognition Grand Challenge database and the BU-3DFE database, in order to validate our proposed methodology and its advantages for 3-D face recognition purposes. We present an analysis regarding the accuracy of our segmentation and landmark detection approaches. Our results were better compared to state-of-the-art works published in the literature. We also performed an evaluation regarding the influence of the segmentation process in our 3-D face recognition system and analyzed the improvements obtained when applying landmark-based techniques to deal with facial expressions.

This paper focuses a comparative evaluation of our framework for 3D face recognition and state-of-theart systems. Our method uses a Simulated Annealing-based approach (SA) for range image registration with the surface interpenetration measure (SIM) as the similarity measure, in order to match two face images. The authentication score is obtained by combining the SIM values corresponding to the matching of four different face regions. Experiments were performed on the FRGC v2 database simulating both verification and identification systems and the obtained results were compared to those reported in the literature. By using all the images in the database, a verification rate of 95.9% was achieved, at a False Acceptance Rate (FAR) of 0.1%. In the identification scenario, a rank-one accuracy of 99.5% was obtained. To our knowledge, this is the best rank-one score obtained on the FRGC v2 database, as compared to previously published results.

The surface interpenetration measure (SIM) was recently proposed as a promising measure for 3D face matching, although using two limited, small range image databases. In this paper we present novel, more extensive experiments using the SIM in a well-known 3D face database available on the biometric experimentation environment (BEE) to confirm qualitatively that the SIM is a effective, discriminatory measure. The experiments were performed based on range image registration by using two different methods: iterative closest point (ICP) and simulated annealing (SA). By computing the SIM after the registration of two 3D face images one can identify if those images come from the same subject or not. With our SA-based approach we obtained high verification rate scores, which is indeed one of the main goals of the Face Recognition Grand Challenge 2006.