Face Recognition Using the Discrete Cosine Transform

Date of Award

12-1991

Document Type

Thesis

Degree Name

Master of Science

Department

Department of Electrical and Computer Engineering

Abstract

The purpose of this study was to improve the feature extraction capability and thus, the recognition accuracy of the AFIT Face Recognition Machine (AFRM). The discrete cosine transform (DCT) was analyzed in depth to determine its image compression and feature extraction capabilities. Features were extracted using a whole image and using sub-blocks of an image. The features extracted were tested for recognition accuracy using a nearest neighbor network classifier. Tests were run to determine if a single person could be distinguished from multiple individuals. Tests were also run to determine if the net could discriminate between multiple individuals. The results were compared with the discrete Fourier transform (DFT) and the Karhunen-Loeve transform (KLT). The DCT results were superior in all cases to those obtained with the DFT and in some case were even superior to those obtained with the KLT.

AFIT Designator

AFIT-GE-ENG-91D-21

DTIC Accession Number

ADA243945

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