Hidden Markov Model-based face recognition using selective attention

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 unifom 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.