Wavelet Based Finger Knuckle and Finger vein Authentication System

  • Sujata Shekhar Kulkarni Yeshwantrao Chavan College of Engineering, Nagpur; RSTM University of Nagpur (India)
  • Ranjana Raut Electronics Department, SGM University Nagpur , India
Keywords: Finger knuckle, Finger vein, Wavelet, Hybrid Wavelet, Authentication, Fusion, Error Equal Rate, ROC curve


Biometrics is the prominent technology for accurate and safe detection of claim identity.  This paper proposes a novel multimodal authentication system using finger knuckle (FK) and finger vein (FV).  Finger Knuckle has unique bending and makes this a distinctive biometric identifier.  The vein pattern of all fingers of human being is not same. Each finger of same person has different vein pattern. It is the hidden part which is not seen by normal eye sight hence less possible to forge. The system consists of proposed prototype finger knuckle and finger vein image capturing devices, formation of own FK and FV image database acquired from proposed devices. Here feature extraction of FK images is based on Walsh Wavelet Transform and FV image on Hybrid Wavelet Transform. Proposed multimodal biometric authentications integrate transformed domain features vector of FK and FV at score level fusion using Bayesian and weighted sum method. The fusion of these two modalities using Bayesian method demonstrated the recognition accuracy of 98.3% and weighted sum 98.5 %.  Various weights of finger knuckle and finger vein affects the recognition accuracy. The better recognition accuracy is obtained at weight of 0.8 and 0.2. The performance index is improved i.e 98.5% and the Error equal rate is 1.5% as compare to unimodal biometric. Error equal rate is reduced by 10% than individual biometric system. For N user with M1 and M2 as test and training samples, for verification of one user, matching complexity is O (M1M2) and for N user O(M1 M2 x N). For identification, (N x M1) test samples and (N x M2) training samples are considered. So matching complexity is O [N (N-1) x M1] for each biometric. Using conventional matching the complexity is O [N (N-1) x M1 M2]. For multimodal biometric using FK and FV, matching complexity is O 2[N (N-1) x M1].  It shows great reduction in matching complexity using the proposed algorithms.   

Author Biography

Sujata Shekhar Kulkarni, Yeshwantrao Chavan College of Engineering, Nagpur; RSTM University of Nagpur (India)


(1) Cheng-BoYu, Quin, LianZhang, Yan-Zhe Cui ,”Finger vein Image Recognition Combining Modified Hausdorff distance with minutia feature matching”, Biomedical Science and Engineering. 2009

(2) Naoto Miura, Akio Nagasaka, Takafumi Miyatake, “Extraction of Finger-Vein Patterns Using Maximum Curvature Points in Image Profiles”, IAPR Conference on Machine Vision Applications, Tsukuba Science City, Japan. 2005,

(3) Arun Ross, Anil k. Jain, “Multimodal Biometrics: An Overview”, Proc. Of 12th European Signal Processing Conference (EUSIPCO), (Vienna Austria), September 2004, pp. 1221-1224.

(4) [20] L.Hong, A.K. Jain, and S. Pankanti, “Can Multibiometrics improves performance?” in Proc. AutoID’99, Summit, NJ, USA. pp. 59 - 64.

(5) Zhang, Lei Zhang, David Zhang and Hailong Zhu, “Online Finger-Knuckle-Print Verification for Personal Authentication”, Biometrics Research Center, Department of computing, The Hong Kong Polytechnic University

(6) M. Indovina, U. Uludag, R. Snelick, A. Mink, A. Jain, “Multimodal Biometric Authentication Methods: A COTS Approach”, National Institute of Standards and Technology, Michigan State University, Appeared in Workshop on MMUA, December 2003.

(7) Ajay Kumar, Ch. Ravikanth , “Personal Authentication using Finger Knuckle Surface”, IEEE Transactions on Information Forensics and Security, vol. 4, no. 1, March. 2009, pp. 98-110,

(8) Lin Zhang, Lei Zhang, David Zhang, Zhenhua Guo, “Phase Congruency induced Local features for Finger-knuckle-print Recognition”, Patten Recognition,45, 2012, pp. 2522-2531.

(9) Michael K.O. Goh, Connie Tee, Andrew B.J. Teoh “BI-modal Palm Print And Knuckle Print Recognition System”, Journal of IT in Asia, vol 3, (2010). (FKP4)

(10) Shrotri A., Rethrekar S.C., Patil, M.H. Debnath, Bhattacharyya, Tai-hoonKim, “Infrared Imaging of Hand Vein Pattern for Biometric Purpose”, Journal of Security Engineering2009.

(11) Naoto Miura, Akio Nagasakat, Takafumi Miyataket, “Automatic Feature Extraction from non-uniform Finger Vein Image and its Application to Personal Identification”, IAPR Workshop on Machine Vision Applications, Japan, 2002 .

(12) Dana Hejitmankova, Radim Dvorak, Martin Drahansky, and Filip Orsag, “A new method of Finger Vein Detection”, International journal of Bio-science and Biotechnology, 1(1) , 2009

(13) Laxmi C., Deepaka , Kandaswamy A., , “An Algorithm For Improved Accuracy in Unimodal Biometric Systems through Fusion of Multiple Features Sets” ,ICGST-GVIP 9(III), 2009

(14) Jing Zhang, Jinfeng Yang, , “ Finger-Vein Image enhancement Based on Combination of Gray-Level Grouping and Circular Gabor Filter “, IEEE,2009

(15) Etta D., Pisano , Shuquan Zoog, Bradley m. Hemminger, Marla DeLuca, R . Eugene Johnston, Keith Muller M. Patricia Braeuning, and Stephen Pizer, “Contrast Limited Adaptive Histogram Equilization Image processing to improve the Detection of Simulated Speculation in dense Mammograms”, Journal of Digital Imaging, 11 (4) , 1998

(16) Stephin M. Pizer,E. Philip Amburn, John D. Austin,Robert Cromartie, Ari Geselowtiz, Trey Greer,Bart Ter Haar Romeny, John B. Zimmerman, and Karel Zuiderveld, “Adaptive histogram Equalization and its Variations”, Computer Vision, Graphics, and Image Processing, 39,355-368, ,

(17) Tomasi C., Manduch R., “Bilateral Filtering for Gray and Color Images”, in Proceedings of IEEE International Conference on Computer Vision, Bombay, India, 1998, pp 237-240

(18) H.B.Kekre and V.A.Bharadi, “Finger Knuckle Verification using Kekre’s Wavelet Transforms”, International Conference & Workshop on Emerging Trends in Technology, ACM Digital Library, New York; 2011. pp 32-37,

(19) H. B. Kekre, Archana Athawale, Dipali Sadavarti, “Algorithm to Generate Wavelet Transform from an Orthogonal Transform”, International Journal of Image Processing; 2011.

(20) Sujata Kulkarni, Ranjana Raut and Pravin Dakhole, “Wavelet Based Modern Finger Knuckle Authentication”, Accepted for publication Published by Elsevier B.V in proceedings 4th International Conference on Eco-friendly Computing and Communication Systems, NIT Kurusetra, India, Elsevier Publications Science Direct Procedia Computer science.

(21) Sujata Kulkarni, Ranjana Raut and Pravin Dakhole , “A Novel Authentication System Based on Hidden Biometric Trait” Accepted for publication in proceedings of International Conference on Computational Modeling and Security (CMS 2016), Banglore, Elsevier Publications , Science Direct Procedia Computer science.

(22) Feifei CUI and Gongping Yang, “Score Level Fusion of Fingerprint and Finger Vein Recognition”, Journal of Computational Information Systems 7: 16 (2011) 5723-5731

How to Cite
Kulkarni, S. S., & Raut, R. (2016). Wavelet Based Finger Knuckle and Finger vein Authentication System. European Journal of Applied Sciences, 4(4), 01. https://doi.org/10.14738/aivp.44.2136