Palm Vein Recognition via Multi-task Loss Function and Attention Layer

TL;DR

This paper presents a robust palm vein recognition method using a CNN with attention and multi-task loss, achieving high accuracy and efficiency suitable for practical deployment.

Contribution

It introduces a multi-task loss function and attention mechanism in a CNN for improved palm vein recognition robustness and accuracy.

Findings

Achieved 98.89% accuracy on prediction set.

Average matching time of 0.13 seconds per pair.

Model demonstrates robustness across different datasets.

Abstract

With the improvement of arithmetic power and algorithm accuracy of personal devices, biological features are increasingly widely used in personal identification, and palm vein recognition has rich extractable features and has been widely studied in recent years. However, traditional recognition methods are poorly robust and susceptible to environmental influences such as reflections and noise. In this paper, a convolutional neural network based on VGG-16 transfer learning fused attention mechanism is used as the feature extraction network on the infrared palm vein dataset. The palm vein classification task is first trained using palmprint classification methods, followed by matching using a similarity function, in which we propose the multi-task loss function to improve the accuracy of the matching task. In order to verify the robustness of the model, some experiments were carried out on datasets from different sources. Then, we used K-means clustering to determine the adaptive matching threshold and finally achieved an accuracy rate of 98.89% on prediction set. At the same time, the matching is with high efficiency which takes an average of 0.13 seconds per palm vein pair, and that means our method can be adopted in practice.