Personal Identification using Local and Global Feature of Finger Vein Patterns using SVM Based Classification - Shrikhande, Santosh P; Fadewar, H.S (International Journal of Computer Sciences and Engineering, 2018)
Personal identification and/or authentication using finger vein pattern is becoming most reliable biometrics in many system securities because of its security, accuracy and convenience. The finger vein pattern based biometrics uses human’s vascular/vein pattern for their unique identification based on the fact that every individual has distinct veins pattern in their fingers. Finger vein pattern biometric trait is robust against the forgery and does not affect due to external factors since it is inherent and hidden under the skin. Therefore, finger vein pattern based biometrics has gained lot of attention of many researchers. This research paper present an approach designed for personal identification using local and global combined features of finger vein pattern. The finger vein pattern local features are extracted using Local Line Binary Pattern and global texture features are extracted using Discrete Wavelet Packet Transform jointly. Feature level fusion method is adopted for constructing the combined feature vector. Then, Support Vector Machine (SVM) based supervised learning algorithm is used for the feature matching and classification. Experiments are conducted using proposed approach on the finger vein image database of Shandong University, China. The experimental results show that proposed approach outperforms the other methods in terms of the recognition accuracy and performance.