Dorsal Hand Vein Identification using Transfer Learning from AlexNet

Dorsal hand vein pattern is a highly secured biometric system that has been used in many applications due to its non-contact attributes. Prior studies focused on investigation of different deep networks for hand vein classification task using different training parameters. It is the aim of this study to propose the use of systematic fine-tuning system for identifying the best parameters value for enhanced model learning efficiency. In this study, pre-trained AlexNet was trained using Bosphorus hand vein database for identification of 100 users. The experiments were carried out using original images, and preprocessed (cropped) images for comparison. The testing accuracies of these datasets were compared following tuning of training parameters, namely training and testing split ratio, number of epochs, mini-batch size and initial learning rate. It was observed that the testing accuracy of the model trained using cropped images is higher than that using the original images. The model from preprocessed dataset shows a good testing accuracy of 96 % using a split ratio of 90:10, epoch 50, mini-batch-size of 10 and an initial learning rate of 0.0001. The performance of our trained model is more superior than many reported results in the field. In future, the performance of this classification system may be further enhanced with automatic search of parameters for improved model training efficiency