Abstract
Traditional face recognition systems have relied on a gallery of still images for learning and a probe of still images for recognition. While the advantage of using motion information in face videos has been widely recognized, computational models for video-based face recognition have only recently gained attention. This chapter reviews some recent advances in this novel framework. In particular, the utility of videos in enhancing performance of image-based tasks (such as recognition or localization) will be summarized. Subsequently, spatiotemporal video-based face recognition systems based on particle filters, hidden Markov models , and system theoretic approaches will be presented. Further, some useful face databases employable by researchers interested in this field will be described. Finally, some open research issues will be proposed and discussed.
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References
Aggarwal, G., Chowdhury, A.R., and Chellappa, R.: A system identification approach for video-based face recognition, In: Proceedings of the 17th International Conference on Pattern Recognition (ICPR), Cambridge, UK, 175–178, 2004.
Aggarwal, G., Veeraraghavan, A., and Chellappa, R.: 3D facial pose tracking in uncalibrated videos, In: International Conference on Pattern Recognition and Machine Intelligence (PReMI), 2005.
Anderson, B. and Moore, J.: Optimal Filtering, Prentice Hall, Englewood Cliffs, New Jersey, 1979.
Arandjelovic, O., and Cipolla, R.: Face recognition from video using the generic shape-illumination manifold , In: Proc. 9th European Conference on Computer Vision, Graz (Austria) (May) Edited by A. Leonardis, H. Bischof and A. Pinz, volume LNCS 3954, 27–40, Springer, 2006.
Bailly-Bailliére, E., Bengio, S., Bimbot, F., Hamouz, M., Kittler, J., Mariéthoz, J., Matas, J., Messer, K., Popovici, V., Porée, F., RuÃz, B., and Thiran, J.P.: The BANCA database and evaluation protocol. Proc. of Audio Video-based Person Authentication, 625–638, 2003.
Bicego, M., Castellani, U., and Murino, V.: Using hidden Markov models and wavelets for face recognition. Proc. of IEEE Int. Conf. on Image Analysis and Processing 52–56, 2003.
Cascia, M.L., Sclaroff, S., and Athitsos, V.: Fast, reliable head tracking under varying illumination: An approach based on registration of texture-mapped 3D models, In: IEEE Trans. on Pattern Analysis and Machine Intelligence, 22 322–336, 2000.
Chan, A.B. and Vasconcelos, N.: Probabilistic kernels for the classification of auto-regressive visual processes, In: IEEE Conference on Computer Vision and Pattern Recognition, 1 846–851, 2005.
Cock, K.D. and Moor, B.D.: Subspace angles between ARMA models, Systems and Control Letters, 46 265–270, 2002.
Chikuse, Y.: Statistics on special manifolds, Lecture Notes in Statistics. Springer, New York, 2003.
Doucet, A., Godsill, S.J., and Andrieu, C.: On sequential Monte Carlo sampling methods for Bayesian filtering, In: Statisical Computing, 10(3) 197–209, 2000.
Doucet, A., Freitas, N.D., and Gordon, N.: Sequential Monte Carlo Methods in Practice, Springer-Verlag, New York, 2001.
Gordon, N.J., Salmond, D.J., and Smith, A.F.M.: Novel approach to nonlinear/non-gaussian Bayesian state estimation, In: IEE Proceedings on Radar and Signal Processing, 140 107–113, 1993.
Hadid, A., and Pietikainen, M.: An experimental investigation about the integration of facial dynamics in video-based face recognition, Electronic Letters on Computer Vision and Image Analysis, 5(1):1–13, 2005
Hager, G.D. and Belhumeur, P.N.: Efficient region tracking with parametric models of geometry and illumination. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20 1025–1039, 1998.
Ho, T.K., Hull, J.J., and Srihari, S.N.: Decision combination in multiple classifier systems, IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(1), 66–75, 1994.
Isard, M. and Blake, A.: Contour tracking by stochastic propagation of conditional density, In: Proceedings of European Conference on Computer Vision 343–356, 1996.
Jebara, T.S. and Pentland, A.: Parameterized structure from motion for 3D adaptive feedback tracking of faces, In: IEEE Conference on Computer Vision and Pattern Recognition, 1997.
Kashyap, R.L.: A Bayesian comparison of different classes of dynamic models using empirical data, IEEE Transactions on Automatic Control, AC-22(5) 715–727, 1977.
Kitagawa, G.: Monte carlo filter and smoother for non-gaussian nonlinear state space models, In: Journal of Computational and Graphical Statistics, 5 1–25, 1996.
Kittler, J., Hatef, M., Duin, R.P.W. and Matas J.: On combining classifiers, IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(3) 226–239, 1998.
Kohir, V.V. and Desai, U.B.: Face recognition using DCT-HMM approach, Proc. Workshop on Advances in Facial Image Analysis and Recognition Technology, 1998.
Lanitis, A., Taylor, C., and Cootes, T.: Automatic interpretation and coding of face images using flexible models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19 743–756, 1997.
Lee, K.C., Ho, J., Yang, M.H., and Kriegman, D.: Video-based face recognition using probabilistic appearance manifolds, In: IEEE Conference on Computer Vision and Pattern Recognition, 2003.
Lee, K.C., Ho, J., Yang, M.H., and Kriegman, D.: Visual tracking and recognition using probabilistic appearance manifolds. Computer Vision and Image Understanding, 99(3): 303–331, 2005.
Li, B. and Chellappa, R.: Face verification through tracking facial features, In: Journal of the Optical Society of America A, 18 2969–2981, 2001.
Liu, J.S. and Chen, R.: Sequential Monte Carlo for dynamic systems, In: Journal of the American Statistical Association, 93 1031–1041, 1998.
Liu, X. and Chen, T.: Video-based face recognition using adaptive hidden Markov models , IEEE Conference on Computer Vision and Pattern Recognition, 2003.
Martin, R.J.: A metric for ARMA processes, In: IEEE Transactions on Signal Processing, 48(4), 1164–1170, 2000.
Messer, K., Kittler, J., Sadeghi, M., Hamouz, M., Kostin, A., Cardinaux, F., Marcel, S., Bengio, S., Sanderson, C., Poh, N., Rodriguez, Y., Czyz, J., Vandendorpe, L., McCool, C., Lowther, S., Sridharan, S., Chandran, V., Palacios, R.P., Vidal, E., Li Bai, LinLin Shen, Yan Wang, Chiang Yueh-Hsuan, Liu Hsien-Chang, Hung Yi-Ping, Heinrichs, A., Muller, M., Tewes, A., von der Malsburg, C., Wurtz, R., Zhenger Wang, Feng Xue, Yong Ma, Qiong Yang, Chi Fang, Xiaoqing Ding, Lucey, S., Goss, R., and Schneiderman, H.: Face Authentication Test on the BANCA Database . ICPR, 4 523–532, 2004.
Messer, K., Kittler, J., Sadeghi, M., Hamouz, M., Kostin, A., Marcel, S., Bengio, S., Cardinaux, F., Sanderson, C., Poh, N., Rodriguez, Y., Kryszczuk, K., Czyz, J., Vandendorpe, L., Ng, J., Cheung, H., and Tang, B.: Face authentication competition on the BANCA database . ICBA 8–15, 2004.
O’Toole, A.J., Roark, D., and Abdi, H.: Recognizing moving faces: A Psychological and Neural Synthesis, In: Trends in Cognitive Sciences, 6, 261–266, 2002.
O’Toole, A.J., Harms, J., Snow, S.L., Hurst, D.R., Pappas, M.R., and Abdi, H.: A video database of moving faces and people. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(5) 812–816, 2005.
Overschee, P.V. and Moor, B.D.: N4SID : Subspace algorithms for the identification of combined deterministic-stochastic systems, In: Automatica, 30 75–93, 1994.
Rabiner, L.: A tutorial on Hidden Markov Models and selected applications in speech recognition, Proc. of IEEE, 77(2) 257–286, 1989.
Roark, D.A., Barrett, S.E., O’Toole, A.J., and Abdi, H.: Learning the moves: The effect of familiarity and facial motion on person recognition across large changes in viewing format, In: Perception,35 761–773, 2006.
Ross, A., Nandakumar, K., and Jain, A.K.: Handbook of Multibiometrics, Springer, New York, 2006.
Schwarz, G.: Estimating the dimension of a model. Annals of Statistics, 6(2):461–464, 1978.
Soatto, S., Doretto, G., and Wu, Y.N.: Dynamic textures, In: International Conference on Computer Vision, 2 439–446, 2001.
Smyth, P.: Clustering sequences with hidden Markov models, In: M. Mozer, M. Jordan, T. Petsche (Eds.), Advances in Neural Information Processing Systems, 9, MIT Press, Cambridge, MA, p. 648, 1997.
Tistarelli, M., Bicego, M., and Grosso, E.: Dynamic face recognition: From Human to Machine Vision, Image and Vision Computing, 27(3) 222–232, 2009.
Turaga, P., Veeraraghavan, A., and Chellappa, R.: Statistical analysis on Stiefel and Grassmann manifolds with applications in computer vision, In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008.
Vishwanathan, S.V.N., Smola, A.J., and Vidal, R.: Binet-Cauchy kernels on dynamical systems and its application to the analysis of dynamic scenes, In: International Journal of Computer Vision, 73(1), 95–119, 2007.
Zhou, S., Krueger, V., and Chellappa, R.: Probabilistic recognition of human faces from video , In: Computer Vision and Image Understanding (CVIU) (special issue on Face Recognition) 91 214–245, 2003.
Zhou, S.K., Chellappa, R., and Moghaddam, B.: Visual tracking and recognition using appearance-adaptive models in particle filters, In: IEEE Transactions on Image Processing 13(11) 1491–1506, 2004.
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Chellappa, R., Bicego, M., Turaga, P. (2009). Video-Based Face Recognition Algorithms. In: Tistarelli, M., Li, S.Z., Chellappa, R. (eds) Handbook of Remote Biometrics. Advances in Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-84882-385-3_8
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DOI: https://doi.org/10.1007/978-1-84882-385-3_8
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