Affiliations: [a] Department of Information Technology, PSG College of Technology, Coimbatore, India
| [b] Department of Electronics and Communication Engineering, K. Ramakrishnan College of Technology, Samayapuram, Trichy, India
| [c] Department of Electronics and Communication Engineering, PSN College of Engineering and Technology, Tirunelveli, Tamil Nadu, India
Correspondence:
[*]
Corresponding author. P. Jayapriya, Department of Information Technology, PSG College of Technology, Coimbatore, 641004, India. E-mail: jayapriyaj2022@gmail.com.
Abstract: In recent years, finger vein recognition has gained a lot of attention and been considered as a possible biometric feature. Various feature selection techniques were investigated for intrinsic finger vein recognition on single feature extraction, but their computational cost remains undesirable. However, the retrieved features from the finger vein pattern are massive and include a lot of redundancy. By using fusion methods on feature extraction approaches involving weighted averages, the error rate is minimized to produce an ideal weight. In this research, a novel combinational model of intelligent water droplets is proposed along with hybrid PCA LDA feature extraction for improved finger vein pattern recognition. Initially, finger vein images are pre-processed to remove noise and improve image quality. For feature extraction, Linear Discriminant Analysis (LDA) and Principle Component Analysis (PCA) are employed to identify the most relevant characteristics. The PCA and LDA algorithms combine features to accomplish feature fusion. A global best selection method using intelligent water drops (GBS-IWD) is employed to find the ideal characteristics for vein recognition. The K Nearest Neighbour Classifier was used to recognize finger veins based on the selected optimum features. Based on empirical data, the proposed method decreases the equal error rate by 0.13% in comparison to existing CNN, 3DFM, and JAFVNet techniques. The overall accuracy of the proposed GBSPSO-KNN is 3.89% and 0.85% better than FFF and GWO, whereas, the proposed GBSIWD-KNN is 4.37% and 1.35% better than FFF and GWO respectively.
Keywords: Principle component analysis, finger vein recognition, linear discriminant analysis, k-nearest neighbor, intelligent water drops
