Computer Recognition of Facial Profiles

Abstract

A system for the recognition of human faces from full profile silhouettes is described. The system is adaptively trained using a novel stack- oriented training procedure which is shown to be quite effective in identifying those feature vectors which are of most importance in the recognition process. Thus the training procedure generally produces authority files having a small number of entries. The feature vectors used are generated from a normalized autocorrelation function expressed in polar coordinates. These feature vectors are shown to be more effective in the recognition process than are the moment invariant features, at least for this problem. Experiments are described in which the system attains a recognition accuracy of 90% in a 10 class problem using 12-dimensional circular autocorrelation feature vectors. It is shown, by further experiments, that these results are no worse than a human observer's accuracy.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1974
Accession Number
ADA004159

Entities

People

  • Gerald J. Kaufman

Organizations

  • Ohio State University

Tags

Communities of Interest

  • Advanced Electronics
  • Air Platforms
  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Cameras
  • Computer Programs
  • Computers
  • Data Science
  • Electrical Engineering
  • Facial Recognition
  • Feature Extraction
  • Identification
  • Image Recognition
  • Information Science
  • Pattern Recognition
  • Photographs
  • Plastic Explosives
  • Recognition
  • Security
  • Training

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Regression Analysis.