Tracking faces of arbitrary views for video annotation

Gang Wei; Ishwar K. Sethi; Nevenka Dimitrova

doi:10.1117/12.411835

29 December 2000 Tracking faces of arbitrary views for video annotation

Gang Wei, Ishwar K. Sethi, Nevenka Dimitrova

Proceedings Volume 4310, Visual Communications and Image Processing 2001; (2000) https://doi.org/10.1117/12.411835
Event: Photonics West 2001 - Electronic Imaging, 2001, San Jose, CA, United States

Abstract

We proposed an omni-face tracking system for video annotation in this paper, which is designed to find faces from arbitrary views in complex scenes. The face detector first locates potential faces in the input by performing skin-tone detection. The subsequent processing consists of two largely independent components, the frontal face module and the side- view face module, responsible for finding frontal-view and side-view faces, respectively. The frontal face module uses a region-based approach wherein regions of skin-tone pixels are analyzed for gross oval shape and the presence of facial features. In contrast, the side-view face module follows an edge-based approach to look for curves similar to a side-view profile. To extract the trajectories of faces, the temporal continuity between consecutive frames within the video shots is considered to speed up the tracking process. The main contribution of this work is being able to find faces irrespective of their poses, whereas contemporary systems deal with frontal-view faces only. Information regarding to human faces is encoded in XML format for semantic video content representation. The effectiveness of human face for video annotation is demonstrated in a TV program classification system that categories the input video clip into predefined types. It is shown that the classification accuracy is improved saliently by the employment of face information.

Citation Download Citation

Gang Wei, Ishwar K. Sethi, and Nevenka Dimitrova "Tracking faces of arbitrary views for video annotation", Proc. SPIE 4310, Visual Communications and Image Processing 2001, (29 December 2000); https://doi.org/10.1117/12.411835

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KEYWORDS

Video

Facial recognition systems

Video surveillance

Classification systems

Multimedia

Sensors

Image segmentation

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