Scale-Adaptive Face Detection and Tracking in Real Time with SSR Filters and Support Vector Machine

In this paper, we propose a method for detecting and tracking faces in video sequences in real time. It can be applied to a wide range of face scales. Our basic strategy for detection is fast extraction of face candidates with a Six-Segmented Rectangular (SSR) filter and face verification by a support vector machine. A motion cue is used in a simple way to avoid picking up false candidates in the background. In face tracking, the patterns of between-the-eyes are tracked while updating the matching template. To cope with various scales of faces, we use a series of approximately 1/√2 scale-down images, and an appropriate scale is selected according to the distance between the eyes. We tested our algorithm on 7146 video frames of a news broadcast featuring sign language at 320 × 240 frame size, in which one or two persons appeared. Although gesturing hands often hid faces and interrupted tracking, 89% of faces were correctly tracked. We implemented the system on a PC with a Xeon 2.2-GHz CPU, running at 15 frames/second without any special hardware.