Abstract
Face recognition in video has gained wide attention due to its role in designing surveillance systems. One of the main advantages of video over still frames is that evidence accumulation over multiple frames can provide better face recognition performance. However, surveillance videos are generally of low resolution containing faces mostly in non-frontal poses. Consequently, face recognition in video poses serious challenges to state-of-the-art face recognition systems. Use of 3D face models has been suggested as a way to compensate for low resolution, poor contrast and non-frontal pose. We propose to overcome the pose problem by automatically (i) reconstructing a 3D face model from multiple non-frontal frames in a video, (ii) generating a frontal view from the derived 3D model, and (iii) using a commercial 2D face recognition engine to recognize the synthesized frontal view. A factorization-based structure from motion algorithm is used for 3D face reconstruction. The proposed scheme has been tested on CMU’s Face In Action (FIA) video database with 221 subjects. Experimental results show a 40% improvement in matching performance as a result of using the 3D models.
Chapter PDF
Similar content being viewed by others
Keywords
References
Pentland, A., Moghaddam, B., Starner, T.: View-based and Modular Eigenspace for Face Recognition. In: Proc. CVPR, pp. 84–91 (1994)
Chai, X., Shan, S., Chen, X., Gao, W.: Local Linear Regression (LLR) for Pose Invariant Face Recognition. In: Proc. AFGR, pp. 631–636 (2006)
Beymer, D., Poggio, T.: Face Recognition from One Example View. In: Proc. ICCV, pp. 500–507 (1995)
Blanz, V., Vetter, T.: Face Recognition based on Fitting a 3D Morphable Model. IEEE Trans. PAMI 25, 1063–1074 (2003)
FaceVACS Software Developer Kit, Cognitec, http://www.cognitec-systems.de
Tomasi, C., Kanade, T.: Shape and motion from image streams under orthography: A factorization method. Int. Journal of Computer Vision 9(2), 137–154 (1992)
Xiao, J., Chai, J., Kanade, T.: A Closed-Form Solution to Non-Rigid Shape and Motion Recovery. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 668–675. Springer, Heidelberg (2004)
Brand, M.: A Direct Method for 3D Factorization of Nonrigid Motion Observation in 2D. In: Proc. CVPR, vol. 2, pp. 122–128 (2005)
Stegmann, M.B.: The AAM-API: An Open Source Active Appearance Model Implementation. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2879, pp. 951–952. Springer, Heidelberg (2003)
Goh, R., Liu, L., Liu, X., Chen, T.: The CMU Face In Action (FIA) Database. In: Zhao, W., Gong, S., Tang, X. (eds.) AMFG 2005. LNCS, vol. 3723, pp. 255–263. Springer, Heidelberg (2005)
Zhao, W., Chellappa, R.: SFS Based View Synthesis for Robust Face Recognition. In: Proc. FGR, pp. 285–292 (2000)
Phillips, P.J., Grother, P., Micheals, R.J., Blackburn, D.M., Tabassi, E., Bone, J.M.: FRVT: 2002: Evaluation Report, Tech. Report NISTIR 6965, NIST (2003)
Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Worek, W.: Preliminary Face Recognition Grand Challenge Results. In: Proc. AFGR, pp. 15–24 (2006)
Lee, K., Ho, J., Yang, M., Kriegman, D.: Video-based face recognition using probabilistic appearance manifolds. CVPR I, 313–320 (2003)
Zhou, S., Krueger, V., Chellappa, R.: Probabilistic recognition of human faces from video. Computer Vision and Image Understanding 91, 214–245 (2003)
Barber, C.B., Dobkin, D.P., Huhdanpaa, H.: The Quickhull Algorithm for Convex Hulls. ACM Trans. Mathematical Software 22(4), 469–483 (1996)
Ullman, S.: The Interpretation of Visual Motion. MIT Press, Cambridge, MA (1979)
Matthews, I., Baker, S.: Active Appearance Models Revisited. International Journal of Computer Vision 60(2), 135–164 (2004)
Maurer, T., Guigonis, D., Maslov, I., Pesenti, B., Tsaregorodtsev, A., West, D., Medioni, G.: Performance of Geometrix ActiveIDTM 3D Face Recognition Engine on the FRGC Data. In: Proc. CVPR, pp. 154–160 (2005)
Tu, J., Huang, T., Tao, H.: Accurate Head Pose Tracking in Low Resolution Video. In: Proc. FGR, pp. 573–578 (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Park, U., Jain, A.K. (2007). 3D Model-Based Face Recognition in Video. In: Lee, SW., Li, S.Z. (eds) Advances in Biometrics. ICB 2007. Lecture Notes in Computer Science, vol 4642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74549-5_113
Download citation
DOI: https://doi.org/10.1007/978-3-540-74549-5_113
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74548-8
Online ISBN: 978-3-540-74549-5
eBook Packages: Computer ScienceComputer Science (R0)