Template Generation by Component Maximization for Real Time Face Detection

摘要

Real-time face detection on video sequences is important in diverse applications such as, man-machine interfaces, face recognition, security and multimedia retrieval. In this work, we present a new method based on the maximization of local components in the directional image to optimize templates for frontal face detection. In the past, several methods for face detection have been developed using face templates. These templates are based on common face features such as eyebrows, eyes, nose and mouth. Templates have been applied to a directional image containing faces computing a line integral to detect faces with high accuracy. In this paper, the maximization of local components in the directional image is used to select new templates optimizing its size and response to a face in the directional image. The method selects common directional vectors in a set of frontal faces to generate the template. The method was tested on 386 images from the Caltech face database and 55 images from the Purdue database. Results were compared to those of the traditional anthropometric templates that contain features from the eyebrow, nose and mouth. Results show that the new templates have significant better performance in the estimation of face size and the line integral value. Face detection reached 97% on the Caltech face database and 98% on the Purdue database. The templates have fewer number of points compared to the traditional anthropometric templates which will lead to lower processing time.