3D shape-based face representation and feature extraction for face recognition

@article{citeulike:590570, abstract = {In this paper, we review and compare 3D face registration and recognition algorithms, which are based solely on 3D shape information and analyze methods based on the fusion of shape features. We have analyzed two different registration algorithms, which produce a dense correspondence between faces. The first algorithm non-linearly warps faces to obtain registration, while the second algorithm allows only rigid transformations. Registration is handled with the use of an average face model, which significantly fastens the registration process. As 3D facial features, we compare the use of 3D point coordinates, surface normals, curvature-based descriptors, 2D depth images, and facial profile curves. Except for surface normals, these feature descriptors are frequently used in state-of-the-art 3D face recognizers. We also perform an in-depth analysis of decision-level fusion techniques such as fixed-rules, voting schemes, rank-based combination rules, and novel serial fusion architectures. The results of the recognition and authentication experiments conducted on the 3D\_RMA database indicate that: (i) in terms of face registration method, registration of faces without warping preserves more discriminatory information, (ii) in terms of 3D facial features, surface normals attain the best recognition performance, and (iii) fusion schemes such as product rules, improved consensus voting and proposed serial fusion schemes improve the classification accuracy. Experimental results on the 3D\_RMA confirm these findings by obtaining \%0.1 misclassification rate in recognition experiments, and \%8.06 equal error rate in authentication experiments using surface normal-based features. It is also possible to improve the classification accuracy by \%2.38 using fixed fusion rules when moderate-level classifiers are used.}, author = {Gokberk, Berk and Irfanoglu, Okan M. and Akarun, Lale }, citeulike-article-id = {590570}, doi = {http://dx.doi.org/10.1016/j.imavis.2006.02.009}, journal = {Image and Vision Computing}, posted-at = {2006-04-18 16:41:07}, priority = {3}, title = {3D shape-based face representation and feature extraction for face recognition}, url = {http://dx.doi.org/10.1016/j.imavis.2006.02.009}, volume = {In Press, Corrected Proof} }

TY - JOUR ID - citeulike:590570 L3 - citeulike-article-id:590570 TI - 3D shape-based face representation and feature extraction for face recognition JF - Image and Vision Computing VL - In Press, Corrected Proof N2 - In this paper, we review and compare 3D face registration and recognition algorithms, which are based solely on 3D shape information and analyze methods based on the fusion of shape features. We have analyzed two different registration algorithms, which produce a dense correspondence between faces. The first algorithm non-linearly warps faces to obtain registration, while the second algorithm allows only rigid transformations. Registration is handled with the use of an average face model, which significantly fastens the registration process. As 3D facial features, we compare the use of 3D point coordinates, surface normals, curvature-based descriptors, 2D depth images, and facial profile curves. Except for surface normals, these feature descriptors are frequently used in state-of-the-art 3D face recognizers. We also perform an in-depth analysis of decision-level fusion techniques such as fixed-rules, voting schemes, rank-based combination rules, and novel serial fusion architectures. The results of the recognition and authentication experiments conducted on the 3D_RMA database indicate that: (i) in terms of face registration method, registration of faces without warping preserves more discriminatory information, (ii) in terms of 3D facial features, surface normals attain the best recognition performance, and (iii) fusion schemes such as product rules, improved consensus voting and proposed serial fusion schemes improve the classification accuracy. Experimental results on the 3D_RMA confirm these findings by obtaining %0.1 misclassification rate in recognition experiments, and %8.06 equal error rate in authentication experiments using surface normal-based features. It is also possible to improve the classification accuracy by %2.38 using fixed fusion rules when moderate-level classifiers are used. AU - Gokberk, Berk AU - Irfanoglu, Okan AU - Akarun, Lale UR - http://dx.doi.org/10.1016/j.imavis.2006.02.009 ER -