Bayesian networks as generative models for face recognition

In this thesis, we address the problem of face modelling by using dedicated statistical generative models, with an application to the face authentication task. Face authentication consists in either accepting or rejecting a user's claim supported by its face image. Classical generative models, such as Gaussian Mixture Models (GMM), Hidden Markov Models (HMM) and their variants have been proved to be successful to tackle this problem. However, these models are not appropriate to the structure of the observed data. In particular, these models implicitly assume independence between features extracted from the image, which is obviously not true in the case of a face. We thus propose new generative models, based on Bayesian Networks, and especially tailored to deal with the object we have to handle: the face. Actually, we would like to exploit as much as a priori knowledge as possible. For this purpose, Bayesian Networks provide an intuitive framework: they allow to encode causal relationships between different kind of random variables, thus enabling to express correlations between different source of information. As a first step, we thus propose a model acting on local observations extracted around salient facial features, which is designed to capture relationships among these pieces of information. The proposed model is shown to be competitive with state-of-the-art approaches based on generative models when applied to the authentication task. At the same level of performance, it is also less complex and thus less time consuming than previous approaches. Besides, and as opposed to classical models, meaningful information could be retrieved from the proposed model. We then extend this model using other sources of information as complementary clues to local features extracted from grayscale face images. Indeed, cognitive studies in face recognition showed that human beings are using various information such as shape, low-resolution representation of the face and also skin color to recognize an individual. We thus proposed new models taking these information into account, and apply them to our authentication problem.

A Novel Statistical Generative Model Dedicated To Face Recognition

Guillaume Heusch, Sébastien Marcel

In this paper, a novel statistical generative model to describe a face is presented, and is applied on the face authentication task. Classical generative models used so far in face recognition, such as Gaussian Mixture Models (GMM) and Hidden Markov Models (HMM) for instance, are making strong assumptions on the observations derived from a face image. Indeed, such models usually assume that local observations are independent, which is obviously not the case in a face. The presented model hence proposes to encode relationships between salient facial features by using a static Bayesian Network. Since robustness against imprecisely located faces is of great concern in a real-world scenario, authentication results are presented using automatically localised faces. Experiments conducted on the XM2VTS and the BANCA databases showed that the proposed approach is suitable for this task, since it reaches state-of-the-art results. We compare our model to baseline appearance-based systems (Eigenfaces and Fisherfaces) but also to classical generative models, namely GMM, HMM and pseudo-2DHMM.

IDIAP2007

A novel statistical generative model dedicated to face recognition

Guillaume Heusch, Sébastien Marcel

In this paper, a novel statistical generative model to describe a face is presented, and is applied to the face authentication task. Classical generative models used so far in face recognition, such as Gaussian Mixture Models (GMMs) and Hidden Markov Models (HMMs) for instance, are making strong assumptions on the observations derived from a face image. Indeed, such models usually assume that local observations are independent, which is obviously not the case in a face. The presented model hence proposes to encode relationships between salient facial features by using a static Bayesian Network. Since robustness against imprecisely located faces is of great concern in a real-world scenario, authentication results are presented using automatically localised faces. Experiments conducted on the XM2VTS and the BANCA databases showed that the proposed approach is suitable for this task, since it reaches state-of-the-art results. We compare our model to baseline appearance-based systems (Eigenfaces and Fisherfaces) but also to classical generative models, namely GMM, HMM and pseudo-2DHMM. (C) 2009 Elsevier B.V. All rights reserved.

2010

Face Authentication with Salient Local Features and Static Bayesian Network

Guillaume Heusch, Sébastien Marcel

In this paper, the problem of face authentication using salient facial features together with statistical generative models is adressed. Actually, classical generative models, and Gaussian Mixture Models in particular make strong assumptions on the way observations derived from face images are generated. Indeed, systems proposed so far consider that local observations are independent, which is obviously not the case in a face. Hence, we propose a new generative model based on Bayesian Networks using only salient facial features. We compare it to Gaussian Mixture Models using the same set of observations. Conducted experiments on the BANCA database show that our model is suitable for the face authentication task, since it outperforms not only Gaussian Mixture Models, but also classical appearance-based methods, such as Eigenfaces and Fisherfaces.

2007