An effective method for detecting facial features and face in human-robot interaction

In this paper, an effective method of facial features detection is proposed for human-robot interaction (HRI). Considering the mobility of mobile robot, it is inevitable that any vision system for a mobile robot is bound to be faced with various imaging conditions such as pose variations, illumination changes, and cluttered backgrounds. To detecting face correctly under such difficult conditions, we focus on the local intensity pattern of the facial features. The characteristics of relatively dark and directionally different pattern can provide robust clues for detecting facial features. Based on this observation, we suggest a new directional template for detecting the major facial features, namely the two eyes and the mouth. By applying this template to a facial image, we can make a new convolved image, which we refer to as the edge-like blob map. One distinctive characteristic of this map image is that it provides the local and directional convolution values for each image pixel, which makes it easier to construct the candidate blobs of the major facial features without the information of facial boundary. Then, these candidates are filtered using the conditions associated with the spatial relationship of the two eyes and the mouth, and the face detection process is completed by applying appearance-based facial templates to the refined facial features. The overall detection results obtained with various color images and gray-level face database images demonstrate the usefulness of the proposed method in HRI applications.