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.
video imaging imaging contention scheme paper extraction feature human activation color shape systems locatability views complexity use presence pattern aspect iter filtering segmentation object region similarity measure profiles exploration hidden markov model side-view faces semantic informal indexing retrieval content-based retrieval skin skin-tone filtering distance image annotation image face detection