Finger-vein identification, a biometric technology that uses vein patterns in the human finger to identify people. In recent years, it has received increasing attention due to its tremendous advantages compared to fingerprint characteristics. Moreover, Deep-Convolutional Neural Networks (Deep-CNN) appeared to be highly successful for feature extraction in the finger-vein area, and most of the proposed works focus on new Convolutional Neural Network (CNN) models, which require huge databases for training, a solution that may be more practicable in real world applications, is to reuse pretrained Deep-CNN models. In this paper, a finger-vein identification system is proposed, which uses Squeezenet pretrained Deep-CNN model as feature extractor from the left and the right finger vein patterns. Then, combines this Deep-based features by using a feature-level Discriminant Correlation Analysis (DCA) to reduce feature dimensions and to give the most relevant features. Finally, these composite feature vectors are used as input data for a Support Vector Machine (SVM) classifier, in an identification stage. This method is tested on two widely available finger vein databases, namely SDUMLA-HMT and FV-USM. Experimental results show that the proposed finger vein identification system achieves significant high mean accuracy rates.
Tea clone classification using deep CNN with residual and densely connections
