Omni-face detection for video/image content description

An omni-face detection scheme for image/video content description is proposed in this paper. It provides the ability to extract high-level features in terms of human activities rather than low-level features like color, texture and shape. The system relies on an omni-face detection system capable of locating human faces over a broad range of views in color images or videos with complex scenes. It uses the presence of skin-tone pixels coupled with shape, edge pattern and face-specific features to locate faces. The main distinguishing contribution of this work is being able to detect faces irrespective of their poses, including frontal-view and side-view, whereas contemporary systems deal with frontal-view faces only. The other novel aspects of the work lie in its iterative candidate filtering to segment objects from extraneous region, the use of Hausdorff distance-based normalized similarity measure to identify side-view facial profiles, and the exploration of hidden Markov model (HMM) to verify the presence of a side-view face. Image and video can be assigned with semantic descriptors based on human face information for later indexing and retrieval.