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

Shinjiro KAWATO  Nobuji TETSUTANI  Kenichi HOSAKA  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E88-D   No.12   pp.2857-2863
Publication Date: 2005/12/01
Online ISSN: 
DOI: 10.1093/ietisy/e88-d.12.2857
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Image Recognition, Computer Vision
Keyword: 
face,  eyes,  detection,  tracking,  SSR-filter,  SVM,  

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Summary: 
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/ 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 320240 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.