ISSN 2394-3777 (Print) ISSN 2394-3785 (Online) Available online at www.ijartet.com International Journal of Advanced Research Trends in Engineering and Technology (IJARTET) Vol. 6, Issue 4, April 2019 All Rights Reserved © 2019 IJARTET 29 Image Processing Methods for 3D Face Recognition D. Malathi 1 , A.Mathangopi 2 , Dr. D. Rajini Girinath 3 P.G Student, Department of CSE, Sri Muthukumaran Institute of Technology, Chennai, India 1 Asst.Prof, Department of CSE, Sri Muthukumaran Institute of Technology, Chennai, India 2 Professor, Department of CSE, Sri Muthukumaran Institute of Technology, Chennai, India 3 Abstract: we present a novel automatic framework to perform 3D face recognition. The proposed method uses a Simulated Annealing-based approach (SA) for range image adjustment with the Surface Interpenetration Measure (SIM), as similarity measure, in order to match two face images. A modified SA approach is proposed taking advantage of constant face regions to better handle facial expressions. 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. Comprehensive experiments were performed on the FRGC v2 database, the largest available database of 3D face images. By using all the images in the database, a verification rate of 96.5% was achieved at a False Acceptance Rate (FAR). The experiments simulated both verification and identification systems and the results compared to those reported by state-of- the-art works. In the identification scenario, rank-one accuracy 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. Keywords: 3D Face Recognition, Surface Interpenetration Measure (SIM), Range Image Registration. I. INTRODUCTION FACE recognition is a very challenging subject. So far, studies in 2D face recognition have reached significant development, but still bear limitations mostly due to pose variation, illumination, and facial expression [13]. One way to overcome such limitations is the use of face depth information. In the 90’s, 3D face recognition stood out due to advances of 3D imaging sensors. However, 3D images also have limitations, such as the presence of noise and difficult image acquisition [7]. A common approach for 3D face recognition is the use of registration techniques to perform range image matching. The Iterative Closest Point (ICP) [5] algorithm, or one of its variants, is usually sought to accomplish this task. The Mean Squared Error (MSE), minimized during the convergence process, is then used to compute the similarity between two face images [8], [9], [14], [17]. This approach can also be employed with deformation techniques to model facial expressions, minimizing its effects on face recognition [17], [19], [20]. The performance of both 2D and 3D face recognition systems can be verified on the Face Recognition Grand Challenge (FRGC) [14]. The FRGC is an international benchmarking composed of six challenging problems with its expected performance guidelines. The third experiment, i.e. ROC III, regards the 3D face recognition problem using a challenging database composed of 4,007 images from 466 subjects displaying different facial expressions. For this experiment, the goal is to achieve a verification rate of 98.0% at a False Acceptance Rate (FAR) of 0.1% [14]. Initially, the 3D face image can be acquired by different techniques, such as laser and structured light scanners [15]. In the preprocessing stage, the input image is smoothed and the face region is automatically detected and segmented. Then, some facial feature points are detected to be used during the matching process [11]. Each segmented face region is matched with its corresponding one in the database. A Simulated Annealing-based approach (SA) for range image registration runs the process by using two robust measures to assess alignment precision: (1) M-estimator Sample Consensus (MSAC) [37], and (2) Surface Interpenetration Measure (SIM) [11]. In previous works, the SIM showed to be a discriminatory measure when working on face images [4], [15]. This paper is organized as follows. Section II discusses the main related works. Section III presents details about the images available in the FRGC v2 database. Section IV describes the preprocessing stage. Section V regards the