Finger Vein Recognition Based on Best Local Difference Code Bit
XI Xiaoming1,2, YIN Yilong3, ZHANG Mengyu1,2, YANG Lu1,2, MENG Xianjing1,2, DU Hengfang1,2
1.School of Computer Science and Technology, Shandong University of Finance and Economics,Jinan 250014 2.Shandong Province Key Laboratory of Digital Media Technology, Shandong University of Finance and Economics, Jinan 250014 3.School of Computer Science and Technology, Shandong University, Jinan 250101
Abstract In finger vein recognition, the local details of the existing code based features are ignored and the discrimination information cannot be fully used. To solve these problems, a best local difference code bit(BLDCB) method is proposed for finger vein recognition. The local difference code(LDC) extraction method is developed to extract the codes. Then, the best bit mining criterion based on relation and inter-class divergence is designed for mining best bits in extracted codes. The conditional probability is calculated for capturing relation between bits and subjects and mining robust bits. Consequently, the intra-class divergence is used for mining discriminative bits from the robust bits, and the selected bits are used as the best bits. Therefore, the best bits are more robust and discriminative. The experimental results on PloyU database and self-constructed finger vein database demonstrate the effectiveness and efficiency of the proposed method.
About author:: (XI Xiaoming(Corresponding author), born in 1987, Ph.D., lecturer. His research interests include machine learning, data mi-ning and biometrics.) (YIN Yilong, born in 1972, Ph.D., profe-ssor. His research interests include machine learning, data mining and biometrics.) (ZHANG Mengyu, born in 1996, undergraduate. Her research interests include machine learning, data mining and biometrics.) (YANG Lu, born in 1988, Ph.D., lecturer. Her research interests include machine lear-ning, data mining and biometrics.) (MENG Xianjing, born in 1986, Ph.D., lecturer. Her research interests include machine learning, data mining and biometrics.) (DU Hengfang, born in 1991, master student. His research interests include machine lear-ning, data mining and biometrics.)
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2017, Vol. 30 
