Automated Face Recognition System

Abstract

In this thesis three variations of an end-to-end face recognition prototype system are developed, implemented and tested. Each version includes real-time image collection, automated segmentation, preprocessing, feature extraction, and classification. The first version uses a Karhunen Loeve Transform (KLT) feature extractor and a K-nearest neighbor classifier. Version two uses the same feature set but utilizes a multilayer perception neural network with a back propagation learning rule. Finally the third version uses a Discrete Cosine Transform as the feature extractor and the K-nearest neighbor as the classifier. Only the KLT versions of the system were tested. The tests were based on three image sets, each collected over multiple days to analyze the effect on recognition accuracy of variations in both the image collection environment and the subjects over time. The first set consisted of 23 Subjects and was taken over a two day period. The second set consisted of four users and was taken over a seven day period. Finally, the third set consisted of 100 images of a single subject collected over several weeks.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1992
Accession Number
ADA258997

Entities

People

  • Kenneth R. Runyon

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Application Software
  • Computer Vision
  • Databases
  • Electrical Engineering
  • Feature Extraction
  • Image Processing
  • Machine Learning
  • Neural Networks
  • Preprocessing
  • Recognition
  • Standards
  • Test Methods
  • Test Sets
  • Theses
  • Two Dimensional
  • Universities
  • Verification

Fields of Study

  • Computer science

Readers

  • Brain and Cognitive Science; Experimental Psychology; Cognitive Neuroscience
  • Computer Vision.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML