Raspberry Pi-based Finger Vein Recognition System using PCANet

Abstract

Finger Vein Recognition System (FVRS) is a biometric technology that identifies or verifies an individual identity based on unique vein patterns. Compared with other biometrics, it is more secure, anti-forgery and hygiene. Thus, it successfully utilized in many authentications nowadays. A feature extraction process is mandatory in maximizing recognition rate of the system. However, majority of feature extraction techniques utilized in FVRS only can provide low-level features, which return an unsatisfied result. A simple deep learning network is proposed in this paper, namely Principal Component Analysis Network (PCANet). PCANet composed of three basic data processing components: 1) PCA filter, 2) binary hashing 3) histograms. PCA is employed for learning multistage filter banks. Binary hashing and block histograms are the steps for indexing and pooling. The features extracted are represented in block-wise histograms. In this paper, PCANet is compared with PCA in term of performance and effectiveness. PCANet retrieved an accuracy of 98.5% under limited training samples, an increase of 21.3% than that of PCA. Factors which impact PCANet are studied to identify the limitations of PCANet. Classifier utilized in FVRS is kNN with Euclidean distance. OpenCV image processing library and C++ language are used in the development. The results returned prove that PCANet performance does not easily influenced by intra-class variability and limited training datasets. Meanwhile, the research noticed about the difficulty in recognition as age increased. The performance evaluation shows that the accuracy of FVRS is 92.67% with the utilization of PCANet. Concluded that PCANet is a promising technique in finger vein recognition.