Eye detection using a deformable template in static images

In this paper, we present a methodology that uses a deformable template to detect the human eye in static images. Geometrically, the used template is represented by two distinct entities: a circumference, that defines the iris contour; and two parabolas, one concave and other convex, that define respectively, the above and below contours of the eye. The geometrical shape of the used template is controlled by a set of eleven control parameters that allows its change in scale, position and orientation. To process the matching between the eye, represented in the input image, and the template used, we consider information obtained from four energy fields. Then, to process the dynamic update of the template parameters, we use an energy function, based on those energy fields, that characterizes the cost of the template deformation. During each of the seven processing phases, we search iteratively for the best combination parameters values that minimize the energy cost of the template deformation. In this paper, we illustrate the functionality of this method by presenting some experimental results, and present some conclusions and perspectives of future work as well. in order to enhance some important aspects of the features to be detected. For example, the iris of the human eye has a strong intensity valley, being very easy to identify its presence in that field. Therefore, this field has the capacity to attract the template used to its correct position. Thus, the eye and the mouth detection in the input image consist on the dynamic and iterative update of the parameters of the used template by matching the same one in the energy fields governed by an energy function. This energy function is defined by a sum of several primitives that uses information obtained from the energy fields, and allows also the characterization of the matching cost during the template deformation. Thus, a low matching cost assures that the correspondence between the template used and the facial feature to be detected is adequate, and consequently that a correct detection was achieved. Otherwise, if the matching cost is high, then the template geometry is very different from the facial feature to be detected and so the matching is not appropriate. Usually, the template matching process in the input image is strategically defined by a set of processing phases, and given an initial set of parameters values, is it very probably that the set of final values obtained is considerably different of the initial one. In this work, we consider the method proposed in (Yuille et al. 1992), for the detection of the human eye in static images, and present the description of the most relevant steps of this method as well as some illustrative experimental examples. This paper is organized as follows: in the next section, we presented the template and energy fields used in our work; then, in section 3, we described the used energy function, the matching process, and the update of the template parameters; in section 4, some experimental results are presented; and finally, in the last section, we addressed some conclusions and perspectives of future work. 2 EYE TEMPLATE AND ENERGY FIELDS