|
|
|
| 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.
|
|
Received: 05 May 2017
|
|
|
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.) |
|
|
|
[1] HUANG D, TANG Y H, WANG Y D, et al. Hand-Dorsa Vein Recognition by Matching Local Features of Multisource Keypoints. IEEE Transactions on Cybernetics, 2015, 45(9): 1823-1837.
[2] ZHANG R K, HUANG D, WANG Y H. Textured Detailed Graph Model for Dorsal Hand Vein Recognition: A Holistic Approach // Proc of the International Conference on Biometrics. Washington, USA: IEEE, 2016. DOI: 10.1109/ICB.2016.7550047.
[3] LEE J C. A Novel Biometric System Based on Palm Vein Image. Pattern Recognition Letters, 2012, 33(12): 1520-1528.
[4] KANG W X, WU Q X. Contactless Palm Vein Recognition Using a Mutual Foreground-Based Local Binary Pattern. IEEE Transactions on Information Forensics and Security, 2014, 9(11): 1974-1985.
[5] YANG J F, SHI Y H, JIA G M. Finger-Vein Image Matching Based on Adaptive Curve Transformation. Pattern Recognition, 2017, 66: 34-43.
[6] KUMAR A, ZHOU Y. Human Identification Using Finger Images. IEEE Transactions on Image Processing, 2012, 21(4): 2228-2244.
[7] MIURA N, NAGASAKA A, MIYATAKE T. Feature Extraction of Finger-Vein Patterns Based on Repeated Line Tracking and Its Application to Personal Identification. Machine Vision and Applications, 2004, 15(4): 194-203.
[8] YANG J F, SHI Y H. Towards Finger-Vein Image Restoration and Enhancement for Finger-Vein Recognition. Information Sciences, 2014, 268: 33-52.
[9] YANG L, YANG G, YIN Y, et al. Finger Vein Recognition with Anatomy Structure Analysis[J/OL].[2017-05-10].ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7882665.
[10] 陈 晖,殷建平,祝 恩.一种扭曲指纹图像的细节点修正方法.计算机研究与发展, 2010, 47(12): 2141-2148.
(CHEN H, YIN J P, ZHU E. A Method to Adjust Minutiae Location and Direction in Nonlinear Distorted Fingerprint Image. Journal of Computer Research and Development, 2010, 47(12): 2141-2148.)
[11] HUANG D, ARDABILIAN M, WANG Y H, et al. 3-D Face Re-cognition Using eLBP-Based Facial Description and Local Feature Hybrid Matching. IEEE Transactions on Information Forensics and Security, 2012, 7(5): 1551-1565.
[12] 周旭东,陈晓红,陈松灿.半配对半监督场景下的低分辨率人脸识别.计算机研究与发展, 2012, 49(11): 2328-2333.
(ZHOU X D, CHEN X H, CHEN S C. Low-Resolution Face Re-cognition in Semi-paired and Semi-supervised Scenario. Journal of Computer Research and Development,2012,49(11): 2328-2333.)
[13] SUN Z N, TAN T N. Ordinal Measures for Iris Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(12): 2211-2226.
[14] XI X M, YANG L, YIN Y L. Learning Discriminative Binary Codes for Finger Vein Recognition. Pattern Recognition, 2017, 66: 26-33.
[15] LEE E C, LEE H C, PARK K R. Finger Vein Recognition Using Minutia-Based Alignment and Local Binary Pattern-Based Feature Extraction. International Journal of Imaging Systems and Technology, 2009, 19(3): 179-186.
[16] LEE E C, JUNG H, KIM D. New Finger Biometric Method Using Near Infrared Imaging. Sensors, 2011, 11(3): 2319-2333.
[17] ROSDI B A, SHING C W, SUANDI S A. Finger Vein Recognition Using Local Line Binary Pattern. Sensors, 2011, 11(12): 11357-11371.
[18] YANG W M, HUANG X L, ZHOU F, et al. Comparative Compe-titive Coding for Personal Identification by Using Finger Vein and Finger Dorsal Texture Fusion. Information Sciences, 2014, 268: 20-32.
[19] SONG W, KIM T, KIM H C, et al. A Finger-Vein Verification System Using Mean Curvature. Pattern Recognition Letters, 2011, 32(11): 1541-1547.
[20] MIURA N, NAGASAKA A, MIYATAKE T. Extraction of Finger-Vein Patterns Using Maximum Curvature Points in Image Profiles. IEICE Transactions on Information and Systems, 2007, E90-D(8): 1185-1194.
[21] YANG G P, XI X M, YIN Y L. Finger Vein Recognition Based on a Personalized Best Bit Map. Sensors, 2012, 12(2): 1738-1757.
[22] OJALA T, PIETIKAINEN M, HARWOOD D. A Comparative Study of Texture Measures with Classification Based on Featured Distributions. Pattern Recognition, 1996, 29(1): 51-59.
[23] XI X M, YANG G P, YIN Y L, et al. Finger Vein Recognition with Personalized Feature Selection. Sensors, 2013, 13(9): 11243-11259. |
|
|
|
2017, Vol. 30 
