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Robust ROI localization based on image segmentation and outlier detection in finger vein recognition

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Abstract

Finger vein is deemed to be a promising biological trait for individual identification. However, partially due to non-uniform collection devices and non-standard collection process, original images are polluted by lots of unfavourable factors. These negative effects increase the burden on image matching. Therefore, Region of Interest (ROI) localization plays an important role in finger vein recognition. Considering that the previous techniques are not common for all kinds of images, we propose a set of methods to obtain the ROI, which is able to remove most of negative factors, preserve more vein information and keep the stability of vein feature with less cost and fewer manual thresholds. More specifically, we propose Simplified Statistical Region Merging (SSRM) with dynamical adjustment of precision parameter to segment an image into finger body and background area. Next, in order to ensure the edge be qualified and further correct the skew angle, the novel Directional Linkage Clustering Method (DLCM) and Parameter Selection (PS) are introduced. Compared with the previous work, the number of thresholds used during the whole process is reduced to only four. The identification EER in experiments is reduced to 0.0476 on all the images in three public databases, which indicates that our method is more superior than the compared methods and performs better in the individual identification.

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References

  1. Agnihotri M, Rathod A, Thapar D, Jaswal G, Tiwari K, Nigam A (2019) Learning domain specific features using convolutional autoencoder: a vein authentication case study using siamese triplet loss network. In: 8th International conference on pattern recognition applications and methods, ICPRAM 2019. SciTePress, pp 778–785

  2. Brindha S (2017) Finger vein recognition. Int J Renew Energy Technol 4:1298–1300

    Google Scholar 

  3. Ehteshami NSM, Tabandeh M, Fatemizadeh E (2012) A new roi extraction method for fkp images using global intensity. In: 6th International symposium on telecommunications (IST). IEEE, pp 1147–1150

  4. Galbally J, Marcel S, Fierrez J (2013) Image quality assessment for fake biometric detection: application to iris, fingerprint, and face recognition. IEEE Trans Image Process 23(2):710–724

    Article  MathSciNet  MATH  Google Scholar 

  5. Harinarayan R, Pannerselvam R, Ali MM, Tripathi DK (2011) Feature extraction of digital aerial images by fpga based implementation of edge detection algorithms. In: 2011 International conference on emerging trends in electrical and computer technology. IEEE, pp 631–635

  6. Kalluri HK, Prasad MV, Agarwal A (2012) Dynamic roi extraction algorithm for palmprints. In: International conference in swarm intelligence. Springer, pp 217–227

  7. Kekre H, Sarode T, Vig R (2012) An effectual method for extraction of roi of palmprints. In: 2012 International conference on communication, information & computing technology (ICCICT). IEEE, pp 1–5

  8. Khellat-Kihel S, Abrishambaf R, Monteiro JL, Benyettou M (2016) Multimodal fusion of the finger vein, fingerprint and the finger-knuckle-print using kernel fisher analysis. Appl Soft Comput 42:439–447

    Article  Google Scholar 

  9. Liang M, Yuan M, Hu X, Li J, Liu H (2013) Traffic sign detection by roi extraction and histogram features-based recognition. In: The 2013 international joint conference on neural networks (IJCNN). IEEE, pp 1–8

  10. Lu Y, Xie SJ, Yoon S, Wang Z, Park DS (2013) An available database for the research of finger vein recognition. In: 2013 6th International congress on image and signal processing (CISP), vol 1. IEEE, pp 410–415

  11. Lu Y, Xie SJ, Yoon S, Yang J, Park DS (2013) Robust finger vein roi localization based on flexible segmentation. Sensors 13(11):14,339–14,366

    Article  Google Scholar 

  12. Lu Y, Wu S, Fang Z, Xiong N, Yoon S, Park DS (2017) Exploring finger vein based personal authentication for secure Iot. Futur Gener Comput Syst 77:149–160

    Article  Google Scholar 

  13. Ma H, Zhang S (2019) Contactless finger-vein verification based on oriented elements feature. Infrared Phys Technol 97:149–155

    Article  Google Scholar 

  14. Matsuda Y, Miura N, Nagasaka A, Kiyomizu H, Miyatake T (2016) Finger-vein authentication based on deformation-tolerant feature-point matching. Mach Vis Appl 27(2):237–250

    Article  Google Scholar 

  15. Nock R, Nielsen F (2004) Statistical region merging. IEEE Trans Pattern Anal Mach Intell 26(11):1452–1458

    Article  Google Scholar 

  16. Peng J, Chan PP (2014) Face liveness detection for combating the spoofing attack in face recognition. In: 2014 International conference on wavelet analysis and pattern recognition. IEEE, pp 176–181

  17. Qiu X, Kang W, Tian S, Jia W, Huang Z (2017) Finger vein presentation attack detection using total variation decomposition. IEEE Trans Inform Forens Secur 13(2):465–477

    Article  Google Scholar 

  18. Rosdi BA, Shing CW, Suandi SA (2011) Finger vein recognition using local line binary pattern. Sensors 11(12):11,357–11,371

    Article  Google Scholar 

  19. Shaheed K, Liu H, Yang G, Qureshi I, Gou J, Yin Y (2018) A systematic review of finger vein recognition techniques. Information 9(9):213

    Article  Google Scholar 

  20. Shin YN, Chun MG, Shin W (2010) A reproducible performance evaluation method for forged fingerprint detection algorithm. In: 2010 International conference on information science and applications. IEEE, pp 1–8

  21. Sierro A, Ferrez P, Roduit P (2015) Contact-less palm/finger vein biometrics. In: 2015 International conference of the biometrics special interest group (BIOSIG). IEEE, pp 1–12

  22. Sun Z, Tan T (2014) Iris anti-spoofing. In: Handbook of biometric anti-spoofing. Springer, pp 103–123

  23. Syazana-Itqan K, Syafeeza A, Saad N, Hamid NA, Saad W (2016) A review of finger-vein biometrics identification approaches. Indian J Sci Technol 9:32

    Article  Google Scholar 

  24. Tizhoosh HR, Gangeh M, Tadayyon H, Czarnota GJ (2016) Tumour roi estimation in ultrasound images via radon barcodes in patients with locally advanced breast cancer. In: 2016 IEEE 13th International symposium on biomedical imaging (ISBI). IEEE, pp 1185–1189

  25. Wang M, Tang D (2017) Region of interest extraction for finger vein images with less information losses. Multimed Tools Appl 76(13):14,937–14,949

    Article  Google Scholar 

  26. Wang M, Tang D, Chen Z (1856) Finger vein roi extraction based on robust edge detection and flexible sliding window. Int J Pattern Recognit Artif Intell 32(04):002

    Google Scholar 

  27. Xi X, Yang L, Yin Y (2017) Learning discriminative binary codes for finger vein recognition. Pattern Recogn 66:26–33

    Article  Google Scholar 

  28. Xie S, Fang L, Wang Z, Ma Z, Li J (2017) Review of personal identification based on near infrared vein imaging of finger. In: 2017 2nd international conference on image, vision and computing (ICIVC). IEEE, pp 206–213

  29. Yang J, Shi Y (2012) Finger–vein roi localization and vein ridge enhancement. Pattern Recogn Lett 33(12):1569–1579

    Article  Google Scholar 

  30. Yang J, Shi Y (2014) Towards finger-vein image restoration and enhancement for finger-vein recognition. Inform Sci 268:33–52

    Article  Google Scholar 

  31. Yang J, Shi Y, Yang J (2009) Finger-vein recognition based on a bank of gabor filters. In: Asian Conference on computer vision. Springer, pp 374–383

  32. Yang J, Zhang B, Shi Y (2012) Scattering removal for finger-vein image restoration. Sensors 12(3):3627–3640

    Article  Google Scholar 

  33. Yang L, Yang G, Yin Y, Xiao R (2013) Sliding window-based region of interest extraction for finger vein images. Sensors 13(3):3799–3815

    Article  Google Scholar 

  34. Yang L, Yang G, Yin Y, Xi X (2014) Exploring soft biometric trait with finger vein recognition. Neurocomputing 135:218–228

    Article  Google Scholar 

  35. Yang WM, Li YC, Liao QM (2014) Fast and robust personal identification by fusion of finger vein and finger-knuckle-print images. In: Applied mechanics and materials, vol 556. Trans Tech Publ, pp 5085–5088

  36. Yang L, Yang G, Zhou L, Yin Y (2015) Superpixel based finger vein roi extraction with sensor interoperability. In: 2015 International conference on biometrics (ICB). IEEE, pp 444–451

  37. Yang J, Shi Y, Jia G (2017) Finger-vein image matching based on adaptive curve transformation. Pattern Recogn 66:34–43

    Article  Google Scholar 

  38. Yang J, Wei J, Shi Y (2019) Accurate roi localization and hierarchical hyper-sphere model for finger-vein recognition. Neurocomputing 328:171–181

    Article  Google Scholar 

  39. Yin Y, Liu L, Sun X (2011) Sdumla-hmt: a multimodal biometric database. In: Chinese conference on biometric recognition. Springer, pp 260–268

  40. Zou H, Zhang B, Tao Z, Wang X (2016) A finger vein identification method based on template matching. In: Journal of physics: conference series, vol 680. IOP Publishing, p 012001

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Acknowledgments

This work was supported by National Key R&D Program of China (2018YFC1603302). We are grateful to the editor and anonymous reviewers for their comments in improving the quality of our article.

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Correspondence to Jianxin Wang.

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Gao, Y., Wang, J. & Zhang, L. Robust ROI localization based on image segmentation and outlier detection in finger vein recognition. Multimed Tools Appl 79, 20039–20059 (2020). https://doi.org/10.1007/s11042-020-08865-y

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  • DOI: https://doi.org/10.1007/s11042-020-08865-y

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