Infrared Handprint Classification Using Deep Convolution Neural Network

Abstract

Infrared handprint image is an image that applies infrared imaging technology to criminal investigation and other special scenes. It can be used to detect traces that cannot be directly observed under visible light.Efficient identification and analysis of handprint are conducive to obtaining more information for solving cases. However, due to thermal diffusion, the depth fuzzy feature of infrared handprint is not conducive to detection and classification, and the convolution neural network technology is widely used in the field of natural image classification because of its excellent feature extraction ability.Therefore, aiming at the problem of fuzzy infrared handprint classification, we design a novel convolution neural network, which includes a convolutional layer, small MBConv block and fully connected layer.We choose EfficientNet which is suitable for infrared handprint classification from classical convolution neural network as our basic network. And propose a small MBConv block to improve the network model, so that the network has fewer training parameters, effectively reduces the problem of over fitting, and improves the classification performance compared with the original model.We use our model for the automatic classification of infrared handprint images. The results show that our model achieves the average accuracyto 95.78% for multi-class classification, which is 2.19% higher than the original model.