Palm-print identification based on deep residual networks

Biometric recognition has been an inseparable part of security and authorization. In the last decade, palm-print has been widely used in security access and person authentication. However, for efficient identity management and access regulation neural network based classification algorithms are required as they provide an efficient means of adaptive feature extraction using back-propagation, leading to better classification results. This paper presents the implementation of various neural networks for an efficient palm-print classification. The model is trained using the ResNet-18, ResNet-50 and ResNet-101 architectures using the PolyU and IIT-Delhi palm-print databases. The evaluation of the performance parameters indicate that the ResNet with SURF features provides the best results in lesser number of epochs. The results obtained are significantly better than the traditional methods.