Abstract
In this paper a novel approach to identity verification, based on the analysis of face video streams, is proposed, which makes use of both physiological and behavioral features. While physical features are obtained from the subject’s face appearance, behavioral features are obtained by asking the subject to vocalize a given sentence. The recorded video sequence is modelled using a Pseudo-Hierarchical Hidden Markov Model, a new type of HMM in which the emission probability of each state is represented by another HMM. The number of states are automatically determined from the data by unsupervised clustering of expressions of faces in the video. Preliminary results on real image data show the feasibility of the proposed approach.
Chapter PDF
References
Bicego, M., Castellani, U., Murino, V.: Using Hidden Markov Models and wavelets for face recognition. In: IEEE. Proc. of Int. Conf on Image Analysis and Processing, pp. 52–56 (2003)
Fine, S., Singer, Y., Tishby, N.: The hierarchical hidden markov model: Analysis and applications. Machine Learning 32, 41–62 (1998)
Hadid, A., Pietikäinen, 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)
Jain, A.K., Dubes, R.: Algorithms for clustering data. Prentice-Hall, Englewood Cliffs (1988)
Knight, B., Johnston, A.: The role of movement in face recognition. Visual Cognition 4, 265–274 (1997)
Lee, K.C., Ho, J., Yang, M.H., Kriegman, D.: Video-based face recognition using probabilistic appearance manifolds. In: Proc. Int. Conf. on Computer Vision and Pattern Recognition (2003)
Li, C.: A Bayesian Approach to Temporal Data Clustering using Hidden Markov Model Methodology. PhD thesis, Vanderbilt University (2000)
Liu, X., Chen, T.: Video-based face recognition using adaptive hidden markov models. In: Proc. Int. Conf. on Computer Vision and Pattern Recognition (2003)
Nefian, A.V., Hayes, M.H.: Hidden Markov models for face recognition. In: Proc. Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), Seattle, pp. 2721–2724 (1998)
O‘Toole, A.J., Roark, D.A., Abdi, H.: Recognizing moving faces: A psychological and neural synthesis. Trends in Cognitive Science 6, 261–266 (2002)
Panuccio, A., Bicego, M., Murino, V.: A Hidden Markov model-based approach to sequential data clustering. In: Caelli, T.M., Amin, A., Duin, R.P.W., Kamel, M.S., de Ridder, D. (eds.) SPR 2002 and SSPR 2002. LNCS, vol. 2396, pp. 734–742. Springer, Heidelberg (2002)
Rabiner, L.: A tutorial on Hidden Markov Models and selected applications in speech recognition. Proc. of IEEE 77(2), 257–286 (1989)
Samaria, F.: Face recognition using Hidden Markov Models. PhD thesis, Engineering Department, Cambridge University (October 1994)
Schwarz, G.: Estimating the dimension of a model. The Annals of Statistics 6(2), 461–464 (1978)
Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: A literature survey. ACM Computing Surveys 35, 399–458 (2003)
Zhou, S., Krueger, V., Chellappa, R.: Probabilistic recognition of human faces from video. Computer Vision and Image Understanding 91, 214–245 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bicego, M., Grosso, E., Tistarelli, M. (2005). Person Authentication from Video of Faces: A Behavioral and Physiological Approach Using Pseudo Hierarchical Hidden Markov Models. In: Zhang, D., Jain, A.K. (eds) Advances in Biometrics. ICB 2006. Lecture Notes in Computer Science, vol 3832. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11608288_16
Download citation
DOI: https://doi.org/10.1007/11608288_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31111-9
Online ISBN: 978-3-540-31621-3
eBook Packages: Computer ScienceComputer Science (R0)