We describe a quantitative evaluation of the performance of different classifiers in the task of automatic age estimation. In this context we generate a statistical model of facial appearance, which is subsequently used as the basis for obtaining a compact parametric description of face images. The aim of our work is to design classifiers that accept the model-based representation of unseen images and produce an estimate of the age of the person in the corresponding face image. For this application we have tested different classifiers: a classifier based on the use of quadratic functions for modeling the relationship between face model parameters and age, a shortest distance classifier and artificial neural network based classifiers. We also describe variations to the basic method where we use age-specific and/or appearance specific age estimation methods. In this context we use age estimation classifiers for each age group and/or classifiers for different clusters of subjects within our training set. In those cases part of the classification procedure is devoted to choosing the most appropriate classifier for the subject/age range in question, so that more accurate age estimates can be obtained. We also present comparative results concerning the performance of humans 1 and computers in the task of age estimation. Our results indicate that machines can estimate the age of a person almost as reliably as humans.
One of the major difficulties encountered in the development of face image processing algorithms, is the possible presence of occlusions that hide part of the face images to be processed. Typical examples of facial occlusions include sunglasses, beards, hats and scarves. In our work we address the problem of restoring the overall shape of faces given only the shape presentation of a small part of the face. In the experiments described in this paper the shape of a face is defined by a series of landmarks located on the face outline and on the outline of different facial features.We describe the use of a number of methods including a method that utilizes a Hopfield neural network, a method that uses Multi-Layer Perceptron (MLP) neural network, a novel technique which combines Hopfield and MLP together, and a method based on associative search. We analyze comparative experiments in order to assess the performance of the four methods mentioned above. According to the experimental results it is possible to recover with reasonable accuracy the overall shape of faces even in the case that a substantial part of the shape of a given face is not visible. The techniques presented could form the basis for developing face image processing systems capable of dealing with occluded faces.1
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