Omni-face detection for video/image content description

ABSTRACT

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.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)

Additional Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Image databases
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Retrieval models

  I. Computing Methodologies
  I.4 IMAGE PROCESSING AND COMPUTER VISION
  I.5 PATTERN RECOGNITION
      I.5.2 Design Methodology
          Subjects: Pattern analysis; Feature evaluation and selection

General Terms:
Design, Experimentation, Management, Measurement, Performance, Theory

Keywords:
content-based retrieval, face detection, image annotation, side-view faces, skin-tone filtering