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A Multi-Agent System for Face Detection and Annotation

  • Rajiv Khosla
  • Ishwar K. Sethi
  • Ernesto Damiani
Chapter
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 582)

Abstract

The purpose of this chapter is to describe the application of the problem solving ontology component of HCVM in the area of face detection and annotation. computer-based aspects of an embedded feature-based multi-agent face detection and annotation system are described in this chapter. Unlike similar feature-based systems that start from searching for facial organs in the images and group them to find faces, the system solves the problem using the problem solving agents of the HCVM in a top-down fashion. The embedded mtilti-agent face detection system makes hypotheses of face locations and seeks evidences to verify them. The five problem solving agents follow a coarse grain to fine grain methodology and work on various features of color images. The coarse to fine top-down methodology is more akin to human perceptions than a bottom-up approach. The higher level problem solving agents like decomposition work as background knowledge for the control and decision agents resulting in improvement of the accuracy and speed of detection. It is also distinct from many other top-down systems in that no re-scaling of the images is needed in the searching process. To improve the detection rate, a unique iterative region-partitioning algorithm is developed in this multi-agent system. The problem solving agents model aspects related to skin-tone region segmentation, noise reduction, candidate face regions location and facial feature extraction/face detection, and annotation. The detection algorithm is invariant to scale and rotation to some degree. The performance of the multi-agent system has been tested with a large number of images and the results obtained thus far are very encouraging.

Keywords

Facial Feature Face Detection Annotation System Image Annotation Skin Region 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2000

Authors and Affiliations

  • Rajiv Khosla
  • Ishwar K. Sethi
  • Ernesto Damiani

There are no affiliations available

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