Face Detection using PSO Template Selection

In this work we present a new method based on PSO (particle swarm optimization) 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, 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 PSO is used to select new templates optimizing its size and response to a face in the directional image. The method was tested on a database composed of two video sequences and compared to the results of the traditional anthropometric templates that contain features from the eyebrow, nose and mouth. Results show that templates selected by PSO have significant better performance in the estimation of face size and the line integral value. In both sequences face detection reached 99% and 100%. The templates have fewer number of points compared to the traditional anthropometric templates which will lead to lower processing time.