An Optimization Algorithm to Improve the Accuracy of Finger Vein Recognition

As people’s daily behavioral activities become more data-based, how to protect personal information security is a crucial consideration for the whole society. Finger vein recognition is becoming an essential means of identification because of its uniqueness, live detection, security, and many other advantages. Although deep learning can make finger vein recognition have an excellent effect. However, the number of samples needed to build a deep network model is too large, and the current authoritative finger vein database cannot reach the minimum number of samples required. The emergence of Muti-Grained Cascade Forest provides a solution to the problem of insufficient sample data and long training time, which can give a new research avenue in feature extraction. In order to obtain higher accuracy, the deep forest algorithm is introduced in this paper to process the finger vein images. Firstly, the image data in the finger vein image database is pre-processed to prepare for the subsequent feature extraction and matching. Then, the deep forest algorithm is used to find the feature points, and the ORB algorithm is used to match the features to obtain the angular information of each matched pair, and the final identity is determined according to the sparse distribution of angles. The accuracy of finger vein recognition based on the deep forest algorithm is 98.40%. By comparing with other machine learning methods for finger vein recognition, the proposed method has a higher accuracy rate.