ABSTRACT
This paper presents an efficient 3D face recognition method to handle facial expression. The proposed method uses the Surfaces Empirical Mode Decomposition (SEMD), facial curves and local shape descriptor in a matching process to overcome the distortions caused by expressions in faces. The basic idea is that, the face is presented at different scales by SEMD. Then the isometric-invariant features on each scale are extracted. After that, the geometric information is obtained on the 3D surface in terms of radial and level facial curves. Finally, the feature vectors on each scale are associated with their corresponding geometric information. The presented method is validated on GavabDB database resulting a rank 1 recognition rate (RR) of 98.9% for all faces with neutral and non-neutral expressions. This result outperforms other 3D expression-invariant face recognition methods on the same database.
- Abate, A.F., Nappi, M., Riccio, D., and Sabatino, G., 2007. 2D and 3D face recognition: A survey. Pattern recognition letters 28, 14, 1885--1906. Google ScholarDigital Library
- Abbad, A., Abbad, K., and Tairi, H., 2017. 3D face recognition: Multi-scale strategy based on geometric and local descriptors. Computers & Electrical Engineering.Google Scholar
- Al-Osaimi, F., Bennamoun, M., and Mian, A., 2009. An expression deformation approach to non-rigid 3D face recognition. International Journal of Computer Vision 81, 3, 302--316. Google ScholarDigital Library
- Amberg, B., Knothe, R., and Vetter, T., 2008. Expression invariant 3D face recognition with a morphable model. In Automatic Face & Gesture Recognition, 2008. FG'08. 8th IEEE International Conference on IEEE, 1--6.Google Scholar
- Aubry, M., Schlickewei, U., and Cremers, D., 2011. The wave kernel signature: A quantum mechanical approach to shape analysis. In Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on IEEE, 1626--1633.Google Scholar
- Ballihi, L., Amor, B.B., Daoudi, M., Srivastava, A., and Aboutajdine, D., 2012. Boosting 3-D-geometric features for efficient face recognition and gender classification. IEEE Transactions on Information Forensics and Security 7, 6, 1766--1779. Google ScholarDigital Library
- Berretti, S., Werghi, N., Del Bimbo, A., and Pala, P., 2013. Matching 3D face scans using interest points and local histogram descriptors. Computers & Graphics 37, 5, 509--525. Google ScholarDigital Library
- Berretti, S., Werghi, N., Del Bimbo, A., and Pala, P., 2014. Selecting stable keypoints and local descriptors for person identification using 3D face scans. The Visual Computer 30, 11, 1275--1292. Google ScholarDigital Library
- Drira, H., Amor, B.B., Srivastava, A., Daoudi, M., and Slama, R., 2013. 3D face recognition under expressions, occlusions, and pose variations. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 9, 2270--2283. Google ScholarDigital Library
- Faltemier, T.C., Bowyer, K.W., and Flynn, P.J., 2008. A region ensemble for 3-D face recognition. IEEE Transactions on Information Forensics and Security 3, 1, 62--73. Google ScholarDigital Library
- Freund, Y. and Schapire, R.E., 1995. A desicion-theoretic generalization of on-line learning and an application to boosting. In European conference on computational learning theory Springer, 23--37. Google ScholarDigital Library
- Huang, D., Ardabilian, M., Wang, Y., and Chen, L., 2012. 3-D face recognition using eLBP-based facial description and local feature hybrid matching. IEEE Transactions on Information Forensics and Security 7, 5, 1551--1565. Google ScholarDigital Library
- Huang, Y., Wang, Y., and Tan, T., 2006. Combining Statistics of Geometrical and Correlative Features for 3D Face Recognition, 90.91-90.10.Google Scholar
- Kakadiaris, I.A., Passalis, G., Toderici, G., Murtuza, M.N., Lu, Y., Karampatziakis, N., and Theoharis, T., 2007. Three-dimensional face recognition in the presence of facial expressions: An annotated deformable model approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 4. Google ScholarDigital Library
- Lei, Y., Bennamoun, M., and El-Sallam, A.A., 2013. An efficient 3D face recognition approach based on the fusion of novel local low-level features. Pattern Recognition 46, 1, 24--37. Google ScholarDigital Library
- Lei, Y., Guo, Y., Hayat, M., Bennamoun, M., and Zhou, X., 2016. A Two-Phase Weighted Collaborative Representation for 3D partial face recognition with single sample. Pattern Recognition 52, 218-237. Google ScholarDigital Library
- Li, X. and Da, F., 2012. Efficient 3D face recognition handling facial expression and hair occlusion. Image and Vision Computing 30, 9, 668--679. Google ScholarDigital Library
- Li, X., Jia, T., and Zhang, H., 2009. Expression-insensitive 3D face recognition using sparse representation. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on IEEE, 2575--2582.Google Scholar
- Lowe, D.G., 2004. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 2, 91--110. Google ScholarDigital Library
- Lu, X. and Jain, A., 2008. Deformation modeling for robust 3D face matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 30, 8, 1346--1357. Google ScholarDigital Library
- Mahoor, M.H. and Abdel-Mottaleb, M., 2009. Face recognition based on 3D ridge images obtained from range data. Pattern Recognition 42, 3, 445--451. Google ScholarDigital Library
- Mian, A.S., Bennamoun, M., and Owens, R., 2008. Keypoint detection and local feature matching for textured 3D face recognition. International Journal of Computer Vision 79, 1, 1--12. Google ScholarDigital Library
- Ming, Y., 2014. Rigid-area orthogonal spectral regression for efficient 3D face recognition. Neurocomputing 129, 445--457. Google ScholarDigital Library
- Moreno, A.B. and Sánchez, A., 2004. GavabDB: a 3D face database. In Proc. 2nd COST275 Workshop on Biometrics on the Internet, Vigo (Spain), 75--80.Google Scholar
- Moreno, A.B., Sanchez, A., Velez, J., and Diaz, J., 2005. Face recognition using 3D local geometrical features: PCA vs. SVM. In Image and Signal Processing and Analysis, 2005. ISPA 2005. Proceedings of the 4th International Symposium on IEEE, 185--190.Google Scholar
- Phillips, P.J., Scruggs, W.T., O'Toole, A.J., Flynn, P.J., Bowyer, K.W., Schott, C.L., and Sharpe, M., 2007. FRVT 2006 and ICE 2006 large-scale results. National Institute of Standards and Technology, NISTIR 7408, 1.Google Scholar
- Smeets, D., Keustermans, J., Vandermeulen, D., and Suetens, P., 2013. meshSIFT: Local surface features for 3D face recognition under expression variations and partial data. Computer Vision and Image Understanding 117, 2, 158--169. Google ScholarDigital Library
- Srivastava, A., Samir, C., Joshi, S.H., and Daoudi, M., 2009. Elastic shape models for face analysis using curvilinear coordinates. Journal of Mathematical Imaging and Vision 33, 2, 253--265. Google ScholarDigital Library
- Szeptycki, P., Ardabilian, M., and Chen, L., 2009. A coarse-to-fine curvature analysis-based rotation invariant 3D face landmarking. In Biometrics: Theory, Applications, and Systems, 2009. BTAS'09. IEEE 3rd International Conference on IEEE, 1--6. Google ScholarDigital Library
- Wang, H., Su, Z., Cao, J., Wang, Y., and Zhang, H., 2012. Empirical mode decomposition on surfaces. Graphical Models 74, 4, 173--183. Google ScholarDigital Library
- Zaharescu, A., Boyer, E., Varanasi, K., and Horaud, R., 2009. Surface feature detection and description with applications to mesh matching. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on IEEE, 373--380.Google Scholar
- Zhao, W., Chellappa, R., Phillips, P.J., and Rosenfeld, A., 2003. Face recognition: A literature survey. ACM computing surveys (CSUR) 35, 4, 399--458. Google ScholarDigital Library
- Zuliani, M., Kenney, C.S., and Manjunath, B., 2005. The multiransac algorithm and its application to detect planar homographies. In Image Processing, 2005. ICIP 2005. IEEE International Conference on IEEE, III-153.Google Scholar
Index Terms
- 3D face recognition in the presence of facial expressions based on empirical mode decomposition
Recommendations
Efficient 3D face recognition handling facial expression and hair occlusion
This paper presents an efficient 3D face recognition method to handle facial expression and hair occlusion. The proposed method uses facial curves to form a rejection classifier and produce a facial deformation mapping and then adaptively selects ...
Three-Dimensional Face Recognition Using Shapes of Facial Curves
We study shapes of facial surfaces for the purpose of face recognition. The main idea is to 1) represent surfaces by unions of level curves, called facial curves, of the depth function and 2) compare shapes of surfaces implicitly using shapes of facial ...
3D face recognition: a survey
3D face recognition has become a trending research direction in both industry and academia. It inherits advantages from traditional 2D face recognition, such as the natural recognition process and a wide range of applications. Moreover, 3D face ...
Comments