3D Face Recognition Using Simulated Annealing and the Surface Interpenetration Measure

Abstract

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.

Fast and Accurate 3D Face Recognition

“…To this end, a distance measure must be defined between the two point clouds. Examples of such distance measures are the Mean Square Error (MSE) between the surfaces and the Surface Interpenetration Measure (SIM), see Silva et al (2005), Queirolo et al (2010). Based on the distance between the point clouds (or the change in distance due to a change in the registration parameters) the registration parameters (θ, φ, γ, t) are updated and the probe is transformed again etc.…”

“…Usually landmarks like the tip of the nose and sometimes the vertical symmetry plane are used to obtain this initial estimate. Examples of one-to-all registration are Maurer et al (2005), Mian et al (2007), Queirolo et al (2010), Faltemier et al (2008b). All of these address only rigid registration.…”

“…This is not necessarily a problem in the verification scenario where only two images must be compared, but it is a problem in the identification scenario where a probe image is compared to a gallery of many images. Therefore the ICP approach is not very fit for identification, as is also pointed out in Faltemier et al (2008a) and Queirolo et al (2010), who incidentally report the highest 3D face identification rates.…”

“…Many of the top ranking papers on 3D face recognition of the last 5 years are based on ICPlike approaches: Faltemier et al (2008a), Kakadiaris et al (2007), Maurer et al (2005), Mian et al (2007), Queirolo et al (2010). Queirolo et al (2010) actually do not use ICP, but Simulating Annealing to obtain a closest fit between two point clouds. The ICP approach and Queirolo's approach, however, have several major disadvantages.…”

“…Like in many other approaches, see e.g. Faltemier et al (2008a) and Queirolo et al (2010), we divide the facial surface into parts that are more or less stable under variation of facial expressions. We found that using multiple overlapping regions and combining them with a simple decision level fusion approach using voting, gives excellent robustness against variations in facial expression.…”