Tracking faces

Author

McKenna, Stephen ; Gong, Shaogang

Author_Institution

Dept. of Comput. Sci., Queen Mary & Westfield Coll., London, UK

fYear

1996

fDate

14-16 Oct 1996

Firstpage

271

Lastpage

276

Abstract

Robust tracking and segmentation of faces is a prerequisite for face analysis and recognition. In this paper we describe an approach to this problem which is well suited to surveillance applications with poorly constrained viewing conditions. It integrates motion-based tracking with model based face detection to produce segmented face sequences from complex scenes containing several people. The motion of moving image contours was estimated using temporal convolution and a temporally consistent list of moving objects was maintained. Objects were tracked using Kalman filters. Faces were detected using a neural network. The essence of the system is that the motion tracker is able to focus attention for a face detection network whilst the latter is used to aid the tracking process

Keywords

Kalman filters; computer vision; convolution; face recognition; image recognition; image segmentation; image sequences; neural nets; Kalman filters; face analysis; face detection network; face recognition; model based face detection; motion tracker; motion-based tracking; neural network; poorly constrained viewing conditions; robust tracking; segmented face sequences; surveillance applications; temporal convolution; temporally consistent list; Convolution; Face detection; Face recognition; Image segmentation; Layout; Motion estimation; Neural networks; Robustness; Surveillance; Tracking;

fLanguage

English

Publisher

ieee

Conference_Titel

Automatic Face and Gesture Recognition, 1996., Proceedings of the Second International Conference on

Conference_Location

Killington, VT

Print_ISBN

0-8186-7713-9

Type

conf

DOI

10.1109/AFGR.1996.557276

Filename

557276