Face Authentication with Salient Local Features and Static Bayesian Network

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.

Presented at:
IEEE / IAPR Intl. Conf. On Biometrics (ICB)
IDIAP-RR 07-04

 Record created 2010-02-11, last modified 2018-03-17

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