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ABSTRACT: This paper proposes a new measure of "distance" between faces. This measure involves the estimation of the set of possible transformations between face images of the same person. The global transformation, which is assumed to be too complex for direct modeling, is approximated by a patchwork of local transformations, under a constraint imposing consistency between neighboring local transformations. The proposed system of local transformations and neighboring constraints is embedded within the probabilistic framework of a two-dimensional hidden Markov model. More specifically, we model two types of intraclass variabilities involving variations in facial expressions and illumination, respectively. The performance of the resulting method is assessed on a large data set consisting of four face databases. In particular, it is shown to outperform a leading approach to face recognition, namely, the Bayesian intra/extrapersonal classifier.
IEEE Transactions on Pattern Analysis and Machine Intelligence 08/2005; 27(7):1157-1171. · 4.91 Impact Factor
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ABSTRACT: We recently introduced a novel approximation of the intractable two-dimensional hidden Markov model (2D HMM), the turbo-HMM (T-HMM), which consists of a set of interconnected horizontal and vertical 1D HMMs. In this paper, we consider the extension of this framework to the continuous state HMM, generally referred to as the state-space model (SSM). We provide efficient approximate answers to the three following problems: (1) how to compute the likelihood of a set of observations; (2) how to find the sequence of states that best "explains" a set of observations; and (3) how to estimate the model parameters given a set of observations. The application of this work to the challenging problem of face recognition, in the presence of large illumination variations, illustrates the potential of our approach.
Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP '04). IEEE International Conference on; 06/2004
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ABSTRACT: While the hidden Markov model (HMM) has been extensively applied to one-dimensional problems, the complexity of its extension to two-dimensions grows exponentially with the data size and is intractable in most cases of interest. We introduce an efficient algorithm for approximate decoding of 2D HMMs, i.e., searching for the most likely state sequence. The basic idea is to approximate a 2D HMM with a turbo-HMM (T-HMM), which consists of horizontal and vertical 1D HMMs that "communicate", and allow iterated decoding (ID) of rows and columns by a modified version of the forward-backward algorithm. We derive the approach and its re-estimation equations. We then compare its performance to another algorithm designed for decoding 2D HMMs: the path constrained variable state Viterbi (PCVSV) algorithm (Li, J. et al., IEEE Trans. on Sig. Processing, vol.48, no.2, 2000). Finally, we combine our approach with PCVSV and show that the combination outperforms each algorithm taken separately.
Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on; 05/2003 · 4.63 Impact Factor
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ABSTRACT: Biometrics is an emerging topic in the field of signal processing. While enabling technologies (e.g. audio, video) for biometrics have mostly used separately, ultimately, biometric technologies could find their strongest role as interwined and complementary pieces of a multi-modal authentication system. In this paper, a short overview of voice, fingerprint, and face authentication algorithms is provided.
