Finger Vein Recognition Based on Spares Representation Classifier
[1]
Shadi Mahmoodi Khaniabadi, Intelligent Biometric Group, School of Electrical and Electronic Engineering, Universiti Sains Malaysia, USM Engineering Campus, 14300 Nibong Tebal, Pulau Pinang, Malaysia.
[2]
Ali Khalili Mobarakeh, School of Engineering, Theatines Campus, University of Málaga, Málaga, Spain.
[3]
Saba Nazari, Intelligent Biometric Group, School of Electrical and Electronic Engineering, Universiti Sains Malaysia, USM Engineering Campus, 14300 Nibong Tebal, Pulau Pinang, Malaysia.
[4]
Sina Ashooritootkaboni, Intelligent Biometric Group, School of Electrical and Electronic Engineering, Universiti Sains Malaysia, USM Engineering Campus, 14300 Nibong Tebal, Pulau Pinang, Malaysia.
[5]
Mohsen Pashna, Centre for Artificial Intelligence and Robotics, University Technology Malaysia, Malaysia.
Nowadays, identification systems have assisted being to avoid, Bank robbery, financial losses, etc. Biometric systems are one of the best technologies that connect identity of individual behavior or their physical characteristics in order to prepare security and safety. Finger vein recognition is one of the recent methods of biometric systems that regarded as matchless and successful way to identify humans based on the physical characteristic of the human finger vein patterns. This paper presents a new novel finger vein recognition method which is combination of principal component analysis (PCA) as a feature extraction and an effective classifier named spares representation classifier (SRC). Further, the significant of the proposed method is proven by comparing SRC result with traditionally classifier named KNN. Finally, experimental results demonstrate that the proposed method has achieved better performance over the same finger vein database. The obtained accuracy of SRC for 1 training and 9 testing finger vein images is 91.14% while for KNN in same condition is 70.86%.
Biometrics, Finger Vein Recognition, K-Nearest Neighbor (KNN), Spares Representation classifier (SRC)
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