Sequential methods for face recognition rely on the analysis of local facial features in a sequential manner,
typically with a raster scan. However, the distribution of discriminative information is not uniform over the facial
surface. For instance, the eyes and the mouth are more informative than the cheek. We propose an extension
to the sequential approach, where we take into account local feature saliency, and replace the raster scan with
a guided scan that mimicks the scanpath of the human eye. The selective attention mechanism that guides the
human eye operates by coarsely detecting salient locations, and directing more resources (the fovea) at interesting
or informative parts. We simulate this idea by employing a computationally cheap saliency scheme, based on
Gabor wavelet filters. Hidden Markov models are used for classification, and the observations, i.e. features
obtained with the simulation of the scanpath, are modeled with Gaussian distributions at each state of the
model. We show that by visiting important locations first, our method is able to reach high accuracy with much
shorter feature sequences. We compare several features in observation sequences, among which DCT coefficients
result in the highest accuracy.
In this paper a novel approach for face authentication is proposed, based on the Hidden Markov Model (HMM) tool. While this technique has been largely and successfully employed in face recognition systems, its use in the authentication context has poorly been investigated. The method proposed in this paper extracts from the image a sequence of partially overlapped images, from which different kinds of simple and quickly computable features are extracted. The face template is obtained by modelling the sequence with a continuous Gaussian Hidden Markov Model. Given an unknown subject, the authentication phase is carried out by thresholding the likelihood of the given face with respect to the HMM template. The proposed approach has been thoroughly tested on the ORL database, also applying different parameters' configurations. A comparison with two other state-of-the-art approaches is also reported. The results obtained are really promising, showing the wide applicability of the Hidden Markov Models methodology.