Tracking faces

Stephen McKenna, Shaogang Gong

Research output: Contribution to conferencePaperpeer-review

38 Citations (Scopus)


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.

Original languageEnglish
Number of pages6
Publication statusPublished - 1 Dec 1996
EventProceedings of the 1996 2nd International Conference on Automatic Face and Gesture Recognition - Killington, VT, USA
Duration: 14 Oct 199616 Oct 1996


ConferenceProceedings of the 1996 2nd International Conference on Automatic Face and Gesture Recognition
CityKillington, VT, USA


  • Face detection
  • Tracking
  • Robustness
  • Face recognition
  • Surveillance
  • Image segmentation
  • Layout
  • Motion estimation
  • Convolution
  • Neural networks

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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