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Hybrid Systems for Facial Analysis and Processing Tasks

  • Srinivas Guttat
  • Harry Wechsler
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 42)

Abstract

We have proposed in this chapter a hybrid architecture for forensic classification and retrieval tasks and we have shown its feasibility using FERET — a large data base of facial images. The specific facial analysis and processing tasks considered herein include (i) surveilling a gallery of images for the presence of specific probes, (ii) contents-based image retrieval (CBIR), (iii) CBIR subject to correct ID (‘match’) displaying specific facial landmarks such as wearing glasses, (iv) gender classification, and (v) ethnic classification. The hybrid architectures, consisting of an ensemble of connectionist networks — radial basis functions (RBF) — and inductive decision trees (DT), combines the merits of ‘holistic’ template matching with those of ‘discrete’ features and classifiers using both positive and negative learning examples. The specific characteristics of our hybrid architecture include (a) query by consensus as provided by ensembles of networks for coping with the inherent variability of the image formation and data acquisition process, (b) categorical classifications using decision trees, and (c) flexible and adaptive thresholds as opposed to ad hoc and hard thresholds.

Keywords

Radial Basis Function Face Image Neural Information Processing System Class Output Radial Basis Function 
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-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Srinivas Guttat
    • 1
  • Harry Wechsler
    • 2
  1. 1.Philips Reasearch LabsBriarcliff ManorUSA
  2. 2.Department of Computer ScienceGeorge Mason UniversityFairfaxUSA

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