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http://dx.doi.org/10.9717/kmms.2019.22.12.1347

Feature Extraction on a Periocular Region and Person Authentication Using a ResNet Model  

Kim, Min-Ki (Dept. of Computer Engineering, Gyeongsang National University Engineering Research Institute)
Publication Information
Journal of Korea Multimedia Society / v.22, no.12, 2019 , pp. 1347-1355 More about this Journal
Abstract
Deep learning approach based on convolution neural network (CNN) has extensively studied in the field of computer vision. However, periocular feature extraction using CNN was not well studied because it is practically impossible to collect large volume of biometric data. This study uses the ResNet model which was trained with the ImageNet dataset. To overcome the problem of insufficient training data, we focused on the training of multi-layer perception (MLP) having simple structure rather than training the CNN having complex structure. It first extracts features using the pretrained ResNet model and reduces the feature dimension by principle component analysis (PCA), then trains a MLP classifier. Experimental results with the public periocular dataset UBIPr show that the proposed method is effective in person authentication using periocular region. Especially it has the advantage which can be directly applied for other biometric traits.
Keywords
Periocular Region; Authentication; CNN; MLP;
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