Demonstration of Palm Vein Pattern Biometric Recognition by Machine Learning

This paper aims to demonstrate the extraction of palm vein pattern features by local binary pattern (LBP) and its different recognition rate by two types of classification methods. The first classification method is by K-nearest neighbour (KNN) while the second method is support vector machine (SVM). Whilst SVM is optimized for direct classification between two classes, the KNN is best for multi-class classification. Based on the biometric recognition framework shared in this paper, both techniques shared comparable performance in terms of the recognition rate. The difference in the recognition rate can only be seen if the LBP features extracted for the classification are different. In general, higher recognition rate can be achieved for palm vein pattern biometric system if all LBP bins are used for the classification, compared to if only selected features are used for the purpose. Best recognition rate that can be achieved by the three datasets demonstrated in this paper are 60%, 70% and 100% respectively for the CASIA, PolyU and self-dataset

Demonstration Of Palm Vein Pattern Biometric Recognition By Machine Learning

This paper aims to demonstrate the extraction of palm vein pattern features by local binary pattern (LBP) and its different recognition rate by two types of classification methods. The first classification method is by K-nearest neighbour (KNN) while the second method is by a support vector machine (SVM). Whilst SVM is optimized for direct classifications between two classes, the KNN is best for multi-class classifications. Based on the biometric recognition framework shared in this paper, both techniques shared comparable performance in terms of the recognition rate. The differences in the recognition rate can only be seen if the LBP features extracted for the classification are different. In general, a higher recognition rate can be achieved for palm vein pattern biometric system if all LBP bins are used for the classification, compared to if only selected features are used for the purpose. The best recognition rate that can be achieved by the three datasets demonstrated in this paper are 60%, 70% and 100% respectively for the CASIA, PolyU and self-dataset. It shows that different input dataset may behave differently even by using the same machine learning approach in its biometric recognition process