Affiliations: [a] Department of Electronics and Communication Engineering, Sengunthar Engineering College, Tiruchengode, Tamilnadu, India
| [b] Department of Electrical and Electronics Engineering, Sona College of Technology, Salem, Tamilnadu, India
Correspondence:
[*]
Corresponding author. P. Gopinath, Department of Electronics and Communication Engineering, Sengunthar Engineering College, Tiruchengode, Tamilnadu, India. E-mail: [email protected].
Abstract: Recognition of finger vein patterns is essential technique that analyses the finger vein patterns to enable accurate authentication of an individual. A proper, accurate and quick learning of patterns is essentially required for improving the classification pattern. It is essential in developing an intelligent algorithm to effectively study and classify the patterns. In this paper, we develop an improved deep learning hybrid model for feature extraction and classification. A dimensional reduction deep neural network (DR-DNN) model has included a dimensional reduction model for extracting the essential features by reducing the dimensionality of feature datasets. A convolutional neural network (CNN) helps in classifying the benign vein patterns from the malignant vein patterns. The effectiveness is compared against existing deep learning classifiers to measure how effective the deep learning model is used for classifying finger vein patterns for biometric authentication. The results shows that the proposed method achieves an accuracy rate of 97.16% for the proposed method, where the other existing methods including CNN, Recurrent Neural Network (RNN) and Deep Neural Nets (DNN) has an accuracy rate of 86%, 80.66% and 88.31%, respectively.
Keywords: Deep neural networks, Deep Convolutional Neural Network, feature extraction, classification, finger vein patterns
