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http://dx.doi.org/10.3745/JIPS.04.0085

Cross-Validation Probabilistic Neural Network Based Face Identification  

Lotfi, Abdelhadi (National Institute of Telecommunication and Information and Communication Technology)
Benyettou, Abdelkader (Dept. of Computing, Faculty of Mathematics and Computing, University of Sciences and Technology of Oran - Mohamed Boudiaf)
Publication Information
Journal of Information Processing Systems / v.14, no.5, 2018 , pp. 1075-1086 More about this Journal
Abstract
In this paper a cross-validation algorithm for training probabilistic neural networks (PNNs) is presented in order to be applied to automatic face identification. Actually, standard PNNs perform pretty well for small and medium sized databases but they suffer from serious problems when it comes to using them with large databases like those encountered in biometrics applications. To address this issue, we proposed in this work a new training algorithm for PNNs to reduce the hidden layer's size and avoid over-fitting at the same time. The proposed training algorithm generates networks with a smaller hidden layer which contains only representative examples in the training data set. Moreover, adding new classes or samples after training does not require retraining, which is one of the main characteristics of this solution. Results presented in this work show a great improvement both in the processing speed and generalization of the proposed classifier. This improvement is mainly caused by reducing significantly the size of the hidden layer.
Keywords
Biometrics; Classification; Cross-Validation; Face Identification; Optimization; Probabilistic Neural Networks;
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