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.

Finger Vein Recognition, Local Binary Pattern, Line Binary Pattern, Discrete Wavelet Packet Transform, Support Vector Machine (SVM)

[1] Eui Chul Lee 1, Hyunwoo Jung and Daeyeoul Kim, “ New Finger Biometric Method Using Near Infrared Imaging”, Sensors, 11, ISSN 1424-8220, pp. 2319-2333, February 2011.
[2] Zhi Liu, Shangling Song, “An Embedded Real-Time Finger-Vein Recognition System for Mobile Devices”, IEEE Transactions on Consumer Electronics, Vol. 58, No. 2, pp. 522-527, May 2012.
[3] S. Damavandinejadmonfared, V. Varadharajan, “Effective finger vein-based authentication: Kernel principal component analysis”, Emerging Trends in Image Processing, Computer Vision and Pattern Recognition, Elsevier, http://dx.doi.org/10.1016/B978-0-12-802045-6.00029-6, 2015.
[4] Ajay Kumar, Yingbo Zhou, “Human Identification using Finger Images”, IEEE Transactions on Image Processing vol. 21, pp. 2228-2244, April 2012.
[5] Santosh P. Shrikhande, Hanumant S. Fadewar, “Personal Identification Using Different Biometrics: A Review”, International Journal of Engineering Research & Technology (IJERT), Vol. 3 Issue 2, pp. 1104-1109, February 2014.
[6] Yingbo Zhou, Ajay Kumar, “Human Identification Using Palm-Vein Images”, IEEE Transactions on Information Forensics and Security, Vol. 6, No. 4, pp. 1259-1274, December 2011.
[7] Lu Yang, Gongping Yang, Yilong Yin and Xiaoming X, “Finger Vein Recognition with Anatomy Structure Analysis”, IEEE Transactions On Circuits And System for Video Technolog,pp. 1-14, 2017.
[8] Jinfeng Yang, Yihua Shi and Jinli Yang, “Personal identification based on finger vein features”, Elsevier, Computers in Human Behavior 27, pp. 1565–1570, November 2010.
[9] Naoto Miura, Akio Nagasaka, Takafumi Miyatake, “Feature extraction of finger vein patterns based on repeated line tracking and its application to personal identification”, Machine Vision and Applications, 15, pp. 194–203, July 2004.
[10] Naoto Miura, Akio Nagasaka, “Extraction of Finger-Vein Patterns Using Maximum Curvature Points in Image Profiles”, IAPR Conference on Machine Vision Applications, Tsukuba Science City, Japan, pp. 347-50, May 2005.
[11] Cheng Bo Yu, Hua Feng Qin, Lian Zhang, Yan-Zhe Cui, “Finger vein image recognition combining modified hausdorff distance with minutiae feature matching”, J. Biomedical Science and Engineering, pp. 261-272, August 2009.
[12] Eui Chul Lee,1 Hyeon Chang Lee,2 Kang Ryoung Park, “Finger Vein Recognition Using Minutia-Based Alignmentand Local Binary Pattern-Based Feature Extraction”, Wiley Periodicals, Inc, Int J Imaging Syst Technoly,Vol.19 ,pp. 180-186, 2009.
[13] Bakhtiar Affendi Rosdi, Chai Wuh Shing and Shahrel Azmin Suandi, “Finger Vein Recognition Using Local Line Binary Pattern”, Sensors 11, pp. 11357-11371, November 2011.
[14] Jinfeng Yang, Yihua Shi, Jinli Yang, “Personal identification based on finger-vein features”, Elsevier, Computers in Human Behavior, Vol. 27, Isuue.5, pp. 1565–1570, October 2011.
[15] Wang Kejun, Liu Jingyu, Popoola Oluwatoyin, Feng Weixing, “Finger Vein Identification Based On 2-D Gabor Filter”, IEEE, 2nd International Conference on Industrial Mechatronics and Automation, (ICIMA 2010) - Wuhan, China, pp. 10-13, May 2010.
[16] Amnart Petpon and Sanun Srisuk, “Face Recognition with Local Line Binary Pattern”, Fifth IEEE International Conference on Image and Graphics, pp. 533-539, 2009.
[17] Yu Lu, Sook Yoon, Shan Juan Xie, Dong Sun Park, “Finger Vein Identification Using Polydirectional Local Line Binary Pattern”, IEEE International Conference TC pp. 61-65, 2013.
[18] Sangita Bharkad, Manesh Kokare, “Rotated Wavelet Filters-Based Fingerprint Recognition”, International Journal of Pattern Recognition and Artificial Intelligence, Vol. 26, No. 3, pp. 1256008-1- 21, Septmber 2012.
[19] Sangita Bharkad, Manesh Kokare, “Fingerprint Matching using Discreet Wavelet Packet Transform”, 3rd IEEE International Advance Computing Conference (IACC), pp.1184-1188, 2013.
[20] B. S. Manjunath and W.Y. Ma, “Texture Features for Browsing and Retrieval of Image Data”, IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 18, No. 8, pp. 837-842, August 1996.
[21] R. Manthalkar, P.K. Biswas, B.N. Chatterji, “Rotation and scale invariant texture features using discrete wavelet packet transform”, Elsevier, Pattern Recognition Letters 24, pp. 2455–2462, 2003.
[22] Santosh P. Shrikhande, H. S. Fadewar, “Finger Vein Recognition Using Discrete Wavelet Packet Transform Based Features”, IEEE, International Conference on Advances in Computing, Communications and Informatics (ICACCI),pp.1646-1651, August 2015.
[23] Manesh Kokare, P. K. Biswas, B. N. Chatterji, “Texture image retrieval using rotated wavelet filters”, Pattern Recognition Letters 28, pp. 1240–1249, February 2007.
[24] Yilong Yin, Lili Liu, and Xiwei Sun., “SDUMLA-HMT - A Multimodal Biometric Database”, Springer-Verlag Berlin Heidelberg, LNCS 7098, pp. 260-268, 2011.
[25] Xianjing Meng, Gongping Yang, Yilong Yin and Rongyang Xiao, “Finger Vein Recognition Based on Local Directional Code”, Sensors, 12, pp.14937-14952, 2012.
[26] Xiaoming Xi, Gongping Yang, Yilong Yin and Xianjing Meng, “Finger Vein Recognition with Personalized Feature Selection” Sensors 2013, 13, pp.11243-11259, ISSN 1424-8220.
[27] Souad Khellat-kihel, Reza abrishambaf, Nuno Cardoso, João Monteiro, Mohamed Benyettou, “Finger Vein Recognition Using Gabor Filter and Support Vector Machine”, IEEE Conference IPAS’14, International Image Processing Applications and Systems, pp.1-6, 2014.
[28] Kang Ryoung Park, “Finger vein Recognition By combining Global and Local Features Based on SVM”, Computing and Informatics, Vol. 30, 2011, pp. 295–309, 2011.
[29] Jian-Da Wu, Chiung-Tsiung Liu, “Finger-vein pattern identification using SVM and neural network technique”, Expert Systems with Applications 38 (2011), pp. 14284–14289, 2011.
[30] Gongping Yang, Xiaoming Xi, and Yilong Yin, “Finger Vein Recognition Based on a Personalized Best Bit Map”, Hand-Based Biometrics Sensors and Systems, 12 pp.1738-1757, February 2012.
[31] Santosh P. Shrikhande, H. S. Fadewar, “ Finger Vein Recognition using Rotated Wavelet Filters”, International Journal of Computer Applications (0975 – 8887) Volume 149 – No.7, pp. 28-33, September 2016.
[32] Nurhafizah Mahri, Shahrel Azmin Sundi Suandi, and Bakhtiar Affendi Rosdi, “Finger Vein Recognition Algorithm Using Phase Only Correlation”, IEEE International Conference on Emerging Techniques and Challenges for Hand-Based Biometrics (ETCHB), pp. 1-6, August 2010.
[33] Jialiang Peng, Qiong L I, Ahmed A, “ Finger vein recognition with Gabor wavelet and Local Binary Pattern”, IEICE Transaction and System Vol. E.96-D, pp.188-1889, August 2013.
[34] Di Cao, Jinfeng Yang, Yihua Shi, Chenghua Xu, “Structure Feature Extraction for Finger-veinRecognition” Second IAPR Asian Conference on Pattern Recognition, pp. 567-571, 2013.