A New Facial Features and Face Detection Method for Human-Robot Interaction

We propose an effective facial features detection method for human– robot interaction (HRI) with indoor mobile robot. In case of human-robot interaction, its vision system has to manage such difficult problems as pose variations, illumination changes, and complex backgrounds, which is caused by the mobility of robot. In this paper, in order to overcome such difficult problems, we suggest a facial feature detection method based on local image area and direct pixel-intensity distributions, in which we propose two novel concepts; the directional template for evaluating intensity distributions and the edge-like blob map with multiple strength intensity. Using this proposed blob map, we show that the locations of major facial features – two eyes and a mouth – can be successfully estimated. Without the information of facial area boundary, final candidate face region is determined by both obtained locations of facial features and weighted correlations with stored facial templates. Our approach is also a flexible algorithm that can be applicable to both color and gray image. In case of color image, faster detection of both facial features and face is feasible by using the chromatic property of facial color. Experimental results from many color images and well-known gray level face database images prove the usefulness of proposed algorithm in human– robot interaction applications.