Cross-Validation Probabilistic Neural Network Based Face Identification
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In this paper a cross-validation algorithm for training probabilistic neural networks (PNNs) is presented inorder to be applied to automatic face identification. Actually, standard PNNs perform pretty well for smalland medium sized databases but they suffer from serious problems when it comes to using them with largedatabases like those encountered in biometrics applications. To address this issue, we proposed in this work anew 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 onlyrepresentative examples in the training data set. Moreover, adding new classes or samples after training doesnot require retraining, which is one of the main characteristics of this solution. Results presented in this workshow a great improvement both in the processing speed and generalization of the proposed classifier. Thisimprovement is mainly caused by reducing significantly the size of the hidden layer.
Abstract
In this paper a cross-validation algorithm for training probabilistic neural networks (PNNs) is presented inorder to be applied to automatic face identification. Actually, standard PNNs perform pretty well for smalland medium sized databases but they suffer from serious problems when it comes to using them with largedatabases like those encountered in biometrics applications. To address this issue, we proposed in this work anew 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 onlyrepresentative examples in the training data set. Moreover, adding new classes or samples after training doesnot require retraining, which is one of the main characteristics of this solution. Results presented in this workshow a great improvement both in the processing speed and generalization of the proposed classifier. Thisimprovement is mainly caused by reducing significantly the size of the hidden layer.
참고문헌 (19)
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