×

zbMATH — the first resource for mathematics

Face recognition under pose variations. (English) Zbl 1104.93059
Summary: In recent years, face recognition has attracted significant attention from the research and commercial communities. Because of the wide variation in face images, face recognition for real applications remains a very challenging problem. A large number of face recognition algorithms, along with their modifications, have been proposed over the past three decades. This paper presents a review of the typical algorithms that aim to overcome one of the main obstacles in the face recognition task, the variations in face pose. These algorithms are categorized and briefly described. Future research challenges in pose-invariant face recognition are also identified.

MSC:
93E35 Stochastic learning and adaptive control
93C95 Application models in control theory
93B60 Eigenvalue problems
68T10 Pattern recognition, speech recognition
PDF BibTeX XML Cite
Full Text: DOI
References:
[1] Phillips, P.J.; Moon, H.; Rizvi, S.; Rauss, P., The FERET evaluation methodology for face recognition algorithms, IEEE trans. pattern anal. Mach. intell., 22, (2000)
[2] D. Blackburn, M. Bone, P.J. Phillips, Face recognition vendor test 2000, Tech. Rep., ⟨http://www.frvt.org⟩.
[3] P.J. Phillips, P.J. Grother, R.J. Michaels, D.M. Blackburn, E. Tabassi, J.M. Bone, Face recognition vendor test 2002: Evaluation report, NISTIR 6965, 2003.
[4] K. Messer, J. Matas, J. Kittler, G. Maitre, XM2VTSDB: The extended M2VTS database, in: International Conference on Audio- and Video-based Person Authentication, 1999, pp. 72-77.
[8] Cognitec Systems GmbH., ⟨http://www.cognitec-systems.de/Contact/contact.html⟩.
[9] Eyematic Interfaces Inc., ⟨http://www.eyematic.com⟩.
[10] Viisage, Littleton, MA, ⟨http://www.viisage.com⟩.
[11] Identix, Minnetonka, MN, ⟨http://www.identix.com⟩.
[12] Chellappa, R.; Wilson, C.L.; Sirohey, S., Human and machine recognition of faces: a survey, Proc. IEEE, 83, 5, 705-741, (1995)
[13] Brunelli, R.; Poggio, T., Face recognition: features versus templates, IEEE trans. pattern anal. Mach. intell., 15, 10, 1042-1052, (1993)
[14] Wiskott, L.; Fellous, J.M.; Von Der Malsburg, C., Face recognition by elastic bunch graph matching, IEEE trans. pattern anal. Mach. intell., 19, 775-779, (1997)
[15] Lanitis, A.; Taylor, C.J.; Cootes, T.F., Automatic face identification system using flexible appearance models, Image vis. comput., 13, 393-401, (1995)
[16] Cootes, T.F.; Edwards, G.J.; Taylor, C.J., Active appearance models, IEEE trans. pattern anal. Mach. intell., 23, 6, 681-685, (2001)
[17] Turk, M.; Pentland, A., Eigenfaces for recognition, J. cognitive neurosci., 3, 1, (1991)
[18] Bellhumer, P.N.; Hespanha, J.; Kriegman, D., Eigenfaces vs. fisherfaces: recognition using class specific linear projection, IEEE trans. pattern anal. Mach. intell. special issue on face recognition, 17, 7, 711-720, (1997)
[19] Zhao, W.; Chellappa, R.; Phillips, P.J.; Rosenfeld, A., Face recognition: a literature survey, ACM comput. surveys, 35, 4, 399-458, (2003)
[20] Lades, M.; Vorbruggen, J.C.; Buhmann, J.; Lange, J.; van der Malsburg, C.; Wurtz, R.P.; Konen, W., Distortion invariant object recognition in the dynamic link architecture, IEEE trans. comput., 42, 300-311, (1993)
[21] V. Blanz, T. Vetter, A morphable model for the synthesis of 3D faces, in: Proceedings of SIGGRAPH’99, 1999, pp. 187-194.
[22] Blanz, V.; Vetter, T., Face recognition based on Fitting a 3D morphable model, IEEE trans. pattern anal. Mach. intell., 25, 9, 1063-1074, (2003)
[23] Blanz, V.; Grother, P.; Phillips, P.J.; Vetter, T., Face recognition based on frontal views generated from non-frontal images, IEEE conf. comput. vis. pattern recogn., 2, 454-461, (2005)
[24] Vetter, T., Learning novel views to a single face image, Int. conf. automat. face gesture recogn., 22-27, (1996)
[25] Vetter, T.; Poggio, T., Linear object classes and image synthesis from a single example image, IEEE trans. pattern anal. Mach. intell., 19, 7, 733-742, (1997)
[26] Kirby, M.; Sirovich, L., Application of the karhunen – loeve procedure for the characterization of human faces, IEEE trans. pattern anal. Mach. intell., 12, 1, 103-108, (1990)
[27] Gokberk, B.; Akarun, L.; Alpaydin, E., Feature selection for pose invariant face recognition, Proc. int. conf. pattern recogn., 4, 306-309, (2002)
[28] D.J. Beymer, Face recognition under varying pose, A. I. Memo No. 1461, Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 1993.
[29] Pentland, A.; Moghaddam, B.; Starner, T., View-based and modular eigenspaces for face recognition, Proc. IEEE conf. comput. vis. pattern recogn., 84-91, (1994)
[30] Cootes, T.; Wheeler, G.; Walker, K.; Taylor, C., View-based active appearance models, Image vis. comput., 20, 657-664, (2002)
[31] P. Huisman, R. van Munster, S. Moro-Ellenberger, R. Veldhuis, A. Bazen, Making 2D face recognition more robust using AAMs for pose compensation, in: Seventh International Conference on Automatic Face and Gesture Recognition (FGR), 2006, pp. 108-113.
[32] Georghiades, A.S.; Kriegman, D.J.; Belhumeur, P.N., Illumination cones for recognition under variable lighting: faces, Proc. IEEE conf. comput. vis. pattern recogn., 52-58, (1998)
[33] Georghiades, A.; Belhumeur, P.; Kriegman, D., From few to many: illumination cone models for face recognition under variable lighting and pose, IEEE trans. pattern anal. Mach. intell., 23, 6, 643-660, (2001)
[34] Okada, K.; von der Malsburg, C., Pose-invariant face recognition with parametric linear subspaces, Proc. IEEE int. conf. automat. face gesture recogn., 64-69, (2002)
[35] Gross, R.; Yang, Jie.; Waibel, A., Growing Gaussian mixture models for pose invariant face recognition, Proc. int. conf. pattern recogn., 1, 1088-1091, (2000)
[36] Beymer, D.J.; Poggio, T., Face recognition from one example view, Proc. int. conf. comput. vis., 500-507, (1995)
[37] Gross, R.; Matthews, I.; Baker, S., Appearance-based face recognition and light-fields, IEEE trans. pattern anal. Mach. intell., 26, 4, 449-465, (2004)
[38] Zhao, W.Y.; Chellappa, R., SFS based view synthesis for robust face recognition, Proc. IEEE int. conf. automat. face gesture recogn., 285-292, (2000)
[39] Liu, X.; Chen, T., Pose-robust face recognition using geometry assisted probabilistic modeling, IEEE comput. soc. conf. comput. vis. pattern recogn., 1, 502-509, (2005)
[40] Hu, Y.; Jiang, D.; Yan, S.; Zhang, L.; zhang, H., Automatic 3D reconstruction for face recognition, Proc. IEEE int. conf. automat. face gesture recogn., 843-848, (2004)
[41] Lee, M.W.; Ranganath, S., Pose-invariant face recognition using a 3D deformable model, Pattern recogn., 36, 1835-1846, (2003)
[42] K.W. Bowyer, K. Chang, P. Flynn, A survey of approaches to three-dimensional face recognition, in: Proceedings of the 17th International Conference on Pattern Recognition (ICPR’04), 2004.
[43] Bowyer, K.W.; Chang, K.; Flynn, P., A survey of approaches and challenges in 3D and multi0modal 3D+2D face recognition, Comput. vis. image understanding, 101, 1-15, (2006)
[44] A. Scheenstra, A. Ruifrok, R.C. Veltkamp, A survey of 3D face recognition methods, in: Proceedings of the Fifth International Conference on Audio and Video Based Biometric Person Authentication, 2005, pp. 891-899.
[45] Cartoux, J.Y.; Lapreste, J.T.; Richetin, M., Face authentication or recognition by profile extraction from range images, Proc. workshop interpret. 3D scenes, 194-199, (1989)
[46] Lee, J.C.; Milios, E., Matching range images of human faces, Int. conf. comp. vis., 722-726, (1990)
[47] G. Medioni, R. Waupotitsch, Face recognition and modeling in 3D, in: Proceedings of IEEE International Workshop on Analysis and Modeling of Faces and Gestures, 2003, pp. 232-233.
[48] A.B. Moreno, A. Sanchez, J.F. Velez, F.J. Diaz, Face recognition using 3D surface-extracted descriptors, in: Proceedings of Irish Machine Vision and Image Processing Conference (IMVIP 2003), 2003.
[49] Y. Lee, K. Park, J. Shim, T. Yi, 3D face recognition using statistical multiple features for the local depth information, in: Proceedings of 16th International Conference on Vision Interface, 2003.
[50] X. Lu, D. Colbry, A.K. Jain, Matching 2.5D scans for face recognition, in: Proceedings of International Conference on Pattern Recognition 2004, 2004, pp. 362-366.
[51] X. Lu, A.K. Jain, Automatic feature extraction for multiview 3D face recognition, in: Seventh International Conference on Automatic Face and Gesture Recognition (FGR), 2006, pp. 585-590.
[52] C. Chua, F. Han, Y.K. Ho, 3D human face recognition using point signature, in: Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition, 2000, pp. 233-238
[53] Bronstein, A.M.; Bronstein, M.M.; Kimmel, R., Three-dimensional face recognition, Int. J. comput. vision, 5-30, (2005)
[54] X. Lu, A.K. Jain, Deformation analysis for 3D face matching, in: Proceedings of Seventh IEEE Workshop on Applications of Computer Vision, 2005, pp. 99-104.
[55] K.I. Chang, K.W. Bowyer, P.J. Flynn, Adaptive rigid multi-region selection for handling expression variation in 3D face recognition, in: Proceedings of IEEE Workshop on Face Recognition Grand Challenge Experiments, 2005.
[56] FERET face database, ⟨http://www.itl.nist.gov/iad/humanid/feret⟩.
[57] ORL face database, ⟨http://www.uk.research.att.com/facedatabase.html⟩.
[58] Yale face database, ⟨http://cvc.yale.edu.projects/yalefaces/yalefaces.html⟩.
[59] AR face database, ⟨http://rvl1.ecn.purdue.edu/aleix/aleix_face_DB.html⟩.
[60] XM2VTS face database, ⟨http://www.ee.surrey.ac.uk/Research/VSSP/xm2vtsdb⟩. · Zbl 1050.68830
[61] CMU PIE face database, ⟨http://www.ri.cmu.edu/projects/project_418.html⟩.
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.