Face Recognition using the HAVNET Neural Network
Abstract
Some cognitive tasks that are easy for humans are not so for computer systems. Face recognition is one of these tasks. A face recognition prototype model using the HAVNET neural network is implemented and tested. The applications of such a model are tremendous and demanding. The prototype model uses a neural network that behaves as a binary pattern classifier. The neural network used, HAVNET, utilizes the Hausdorff distance as a metric of similarity between patterns and it employs a learned version of the Voronoi surface to perform the comparison [1]. Different human faces' images are used for training and testing the model. The recognition results as well as the different sensitive factors that affect the recognition process are discussed.
Recommended Citation
U. M. Altaf and C. H. Dagli, "Face Recognition using the HAVNET Neural Network," Proceedings of SPIE - The International Society for Optical Engineering, vol. 2492, pp. 873 - 883, Society of Photo-optical Instrumentation Engineers, Apr 1995.
The definitive version is available at https://doi.org/10.1117/12.205199
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Face recognition; Hausdorff distance; HAVNET
International Standard Serial Number (ISSN)
1996-756X; 0277-786X
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2024 Society of Photo-optical Instrumentation Engineers, All rights reserved.
Publication Date
06 Apr 1995
