Finger vein identification based on transfer learning of AlexNet

Nowadays finger vein-based validation systems are getting extra attraction among other authentication systems due to high security in terms of ensuring data confidentiality. This system works by recognizing patterns from finger vein images and these images are captured using a camera based on near-infrared technology. In this research, we focused finger vein identification system by using our own finger vein dataset, we trained it with transfer learning of AlexNet model and verified by test images. We have done three different experiments with the same dataset but different sizes of data. Therefore, we obtained varied predictability with 95% accuracy from the second experiment

Pre-trained based CNN model to identify finger vein

In current biometric security systems using images for security authentication, finger vein-based systems are getting special attention in particular attributable to the facts such as insurance of data confidentiality and higher accuracy. Previous studies were mostly based on finger-print, palm vein etc. however, due to being more secure than fingerprint system and due to the fact that each person's finger vein is different from others finger vein are impossible to use to do forgery as veins reside under the skin. The system that we worked on functions by recognizing vein patterns from images of fingers which are captured using near Infrared (NIR) technology. Due to the lack of an available database, we created and used our own dataset which was pre-trained using transfer learning of AlexNet model and verification is done by applying correct as well as incorrect test images. The result of deep convolutional neural network (CNN) based several experimental results are shown with training accuracy, training loss, Receiver Operating Characteristic (ROC) Curve and Area Under the Curve (AUC)

Convolutional neural network-based finger vein recognition using near infrared images

Convolutional Neural Network (CNN) is opening new horizons in biometrics-based authentication field and finger vein recognition is the prominent one which can provide the best possible security system depending on this aforementioned technology. In this paper, we used 5 convolutional layers and 4 fully-connected layers where our developed network has shown the capability to produce the result with almost 100% accuracy rate which became possible due to the fact that deep learning, an end-to-end system is used which performs better in a lot of aspects in comparison to conventional techniques.Convolutional Neural Network (CNN) is opening new horizons in biometrics-based authentication field and finger vein recognition is the prominent one which can provide the best possible security system depending on this aforementioned technology. In this paper, we used 5 convolutional layers and 4 fully-connected layers where our developed network has shown the capability to produce the result with almost 100% accuracy rate which became possible due to the fact that deep learning, an end-to-end system is used which performs better in a lot of aspects in comparison to conventional techniques