NDLI: Design of radial basis function network as classifier in face recognition using eigenfaces

Content Provider	IEEE Xplore Digital Library
Author	Thomaz, C.E. Feitosa, R.Q. Veiga, A.
Copyright Year	1998
Description	Author affiliation: Dept. de Engenharia Eletrica, Pontificia Univ. Catolica do Rio de Janeiro, Brazil (Thomaz, C.E.)
Abstract	In this paper we investigate alternative designs of a radial basis function network acting as classifier in a face recognition system. The inputs to the RBF network are the projections of a face image over the principal components. A database of 250 facial images of 25 persons is used for training and evaluation. Two RBF designs are studied: the forward selection and the Gaussian mixture model. Both designs are also compared to the conventional Euclidean and Mahalanobis classifiers. A set of experiments evaluates the recognition rate of each method as a function of the number of principal components used to characterize the image samples. The results of the experiments indicate that the Gaussian mixture model RBF achieves the best performance while allowing less neurons in the hidden layer. The Gaussian mixture model approach shows also to be less sensitive to the choice of the training set.
Starting Page	118
Ending Page	123
File Size	71504
Page Count	6
File Format	PDF
ISBN	0818686294
DOI	10.1109/SBRN.1998.731006
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1998-12-09
Publisher Place	Brazil
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Radial basis function networks Intelligent networks Face recognition Neurons Image databases Fingerprint recognition Pattern recognition Electronic switching systems Authentication Telephony
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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