Evaluation of Fine Tuning and Feature Extraction methods in Biometric Periocular Recognition
Resumo
The aim of this paper is to evaluate the performance of Transfer Learning techniques applied in Convolucional Neural Networks for biometric periocular classification. Two aspects of Transfer Learning were evaluated: the technique known as Fine Tuning and the technique known as Feature Extraction. Two CNN architectures were evaluated, the AlexNet and the VGG-16, and two image databases were used. These two databases have different characteristics regarding the method of acquisition, the amount of classes, the class balancing, and the number of elements in each class. Three experiments were conducted to evaluate the performance of the CNNs. In the first experiment we measured the Feature Extraction accuracy, and in the second one we evaluated the Fine Tuning performance. In the third experiment, we used the AlexNet for Fine Tuning in one database, and then, the FC7 layer of this trained CNN was used for Feature Extraction in the other database. We concluded that the data quality (the presence or not of class samples in the training set), class imbalance (different number of elements in each class) and the selection method of the training and testing, directly influence the CNN accuracy. The Feature Extraction method, by being more simple and does not require network training, has lower accuracy than Fine Tuning. Furthermore, Fine Tuning a CNN with periocular's images from one database, doesn't increase the accuracy of this CNN in Feature Extraction mode for another periocular's database. The accuracy is quite similar to that obtained by the original pre-trained network
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