Cross-Validation Probabilistic Neural Network Based Face Identification

Abdelhadi Lotfi and Abdelkader Benyettou
Volume: 14, No: 5, Page: 1075 ~ 1086, Year: 2018
Keywords: Biometrics, Classification, Cross-Validation, Face Identification, Optimization, Probabilistic Neural Networks
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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.

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Cite this article
IEEE Style
A. L. A. Benyettou, "Cross-Validation Probabilistic Neural Network Based Face Identification," Journal of Information Processing Systems, vol. 14, no. 5, pp. 1075~1086, 2018. DOI: 10.3745/JIPS.04.0085.

ACM Style
Abdelhadi Lotfi and Abdelkader Benyettou. 2018. Cross-Validation Probabilistic Neural Network Based Face Identification, Journal of Information Processing Systems, 14, 5, (2018), 1075~1086. DOI: 10.3745/JIPS.04.0085.