Deep-learning-based photoacoustic finger vein image recognition

Compared to traditional identification technology, biometric technology has attracted a lot of attention because it highlights the special physiological characteristics and behavioural patterns of the human body. Among many biometric technologies, finger vein recognition technology is highly resistant to forgery, reliable, and unaffected by changes in skin surface conditions. In this study, a specially designed 128-ring array fast photoacoustic imaging system is used to accurately capture finger vein, muscle and skin images for information acquisition. The image segmentation algorithm in deep learning was used to remove the noise from the images, and then using the uniqueness of finger veins and muscle structures between individuals, the features of each image were automatically extracted using a 2D convolutional neural network to produce the classification results for each image, which were then ensemble to produce the identification results for each subject.