RandAugment With Knowledge Distillation For Finger-Vein Recognition

The current society is in an era of vigorous innovation and development in digital media and artificial intelligence. We often see the sharing of our media, and this makes the external biometric information be at a high risk of exposure. However, once the biometrics are leaked, it is difficult to update and modify them. Therefore, a biological feature that is difficult to be exposed is necessary in the future. The vein in the human body has this feature, which makes it advantageous for live imaging. With the steady development of deep learning (DL) technology, an identification model can easily have an extremely high accuracy rate, but there are also disadvantages like a high parameter volume, calculation volume, and storage volume. These disadvantages cause the model to be unable to be effectively implemented in the real world. To solve the problems, this paper proposes a model training strategy combined with automatic augmentation, to achieve the advantages of reducing the amount of model parameter and improving the accuracy of the model. As results, the method of this paper can improve the accuracy of the model by 16.9% without changing the parameter quantity.