Direction of vein mapping in forensic identification: a comprehensive review

Neha Badhwar; Khadija Murtala Mukaddas; Vhagyashree Neogi; Ameesha Swapna; Bill Fallah Fomba

Direction of vein mapping in forensic identification: a comprehensive review

Data publikacji: 19.12.2024

Problems of Forensic Sciences (Z Zagadnień Nauk Sądowych), 2024, 139, s. 227 - 244

https://doi.org/10.4467/12307483PFS.24.016.20915

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Tytuły

Direction of vein mapping in forensic identification: a comprehensive review

Abstrakt

Vein mapping can be used to identify possible suspects using matching learning algorithms. Since vasculature deep in the skin cannot be visualized by naked eyes, the features extracted usually by converting to near infrared images which gives best track recovery with little noise. Two decades, ago the premise for the use of vein patterns for identification emerged in the forensic field. Researchers are proposing innovative approaches and methods utilized to improve the recognition, quality, classification, and extraction of viable vein patterns from images. Deep learning algorithms such as convolution neural network (CNN ), K-nearest network, autoencoders are being used to extract venous features with ease especially when analyzing image forensic evidence. This paper provides an overview of recently proposed finger vein, dorsal hand vein, wrist vein and hybrid systems and highlights their performance and real-life application.

Słowa kluczowe

Vein mapping, Algorithms, Feature extraction, Identification, Deep learning, Infrared

Udział autorów

Wszyscy autorzy mieli wkład w opracowanie, projektowanie i strukturę manuskryptu. Neha Badhwar i Khadija Murtala Mukaddas – opracowanie konceptu, redakcja końcowa i korekta. Bill Fallah Fomba – wstęp. Vhagyashree Neogi – rozdział o żyłach ręki i palców. Ameesha S. – dyskusja i wnioski. Khadija Murtala Mukaddas – pozostałe rozdziały. Wszyscy autorzy przeczytali i zaakceptowali ostateczną wersję manuskryptu.

Bibliografia

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1. Wang Y, Zhang K, Shark LK. Personal identification based on multiple keypoint sets of dorsal hand vein images. IET Bio. 2014;3(4):234-45.

2. Dargan S, Kumar M. A comprehensive survey on the biometric recognition systems based on physiological and behavioral modalities. Expert Syst Appl. 2020 Apr 1;143:113114.

3. Li X, Li C, Rahaman MM, Sun H, Li X, Wu J, et al. A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches. Artif Intell Rev. 2022 Aug 1;55(6):4809-78.

4. S haheed K, Liu H, Yang G, Qureshi I, Gou J, Yin Y. A systematic review of finger vein recognition techniques. Information. 2018 Aug 24;9(9):213.

5. Galeandro AI, Quistelli G, Scicchitano P, Gesualdo M, Zito A, Caputo P, et al. Doppler ultrasound venous mapping of the lower limbs. Vasc Health Risk Manag. 2012;8(1):59-64.

6. Abd Rahman AB, Juhim F, Chee FP, Bade A, Kadir F. Near infrared illumination optimization for vein detection: hardware and software approaches. Appl Sci. 2022;12(21):11173.

7. Matkowski WM, Chan FKS, Kong AW K. A study on wrist identification for forensic investigation. Image Vis Comput. 2019 Aug 1;88:96-112.

8. Pititheeraphab Y, Thongpance N, Aoyama H, Pintavirooj C. Vein pattern verification and identification based on local geometric invariants constructed from minutia points and augmented with barcoded local feature. Appl Sci. 2020 May 1;10(9):3192.

9. Aiken RS . Vein pattern analysis – a comparison of two vein imaging modalities (Doctoral dissertation). University of Dundee; 2014.

10. Kang W, Liu H, Luo W, Deng F. Study of a Full-view 3D finger vein verification technique. IEEE Trans Inf Forensics Secur. 2020;15:1175-89.

11. Shahzad A, Saad M, Walter N, Malik A, Meriaudeau F. A review on subcutaneous veins localization using imaging techniques. Curr Med Imaging Rev. 2014;10(2):125-33.

12. W ang L, Leedham G, Cho SY. Infrared imaging of hand vein pattern for biometric purposes. IET Computer Vision. 2007;1(3):113-122.

13. Tang C, Kong AW K, Craft N. Uncovering vein patterns from color skin images for forensic analysis. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Colorado Springs: IEEE; 2011. p. 665-72.

14. Sharma N, Hefeeda M. Hyperspectral reconstruction from RGB images for vein visualization. In: MMSys 2020 – Proceedings of the 2020 Multimedia Systems Conference. Association for Computing Machinery, Inc; 2020. p. 77-87.

15. Damak W, Trabelsi RB, Damak MA, Sellami D. Dynamic ROI extraction method for hand vein images. IET Computer Vision. 2018;12(5):586-95.

16. Zhang H, Tang C, Li X, Kong AW K. A study of similarity between genetically identical body vein patterns. In: IEEE Workshop on Computational Intelligence in Biometrics and Identity Management, CIBIM. Orlando, FL; 2015. p. 151-9.

17. Zhang H, Tang C, Kong AW K, Craft N. Matching vein patterns from color images for forensic investigation. In: 2012 IEEE 5th International Conference on Biometrics: Theory, Applications and Systems (BTAS ). Arlington, VA: IEEE; 2012. p. 77-84.

18. Hartung B, Rauschning D, Schwender H, Ritz-Timme S. A simple approach to use hand vein patterns as a tool for identification. Forensic Sci Int. 2020 Feb 1;307:110115.

19. Cimen ME, Boyraz OF, Yildiz MZ, Boz AF. A new dorsal hand vein authentication system based on fractal dimension box counting method. Optik (Stuttg). 2021 Jan 1;226:165438.

20. Vitek M, Rot P, Štruc V, Peer P. A comprehensive investigation into sclera biometrics: a novel dataset and performance study. Neural Comput Appl. 2020 Dec 1;32(24):17941-55.

21. Hussain W, Rasool N, Yaseen M. AD VIT: Using the potentials of deep representations incorporated with gridbased features of dorsum vein patterns for human identification. Forensic Sci Int. 2020 Aug 1;313:110345.

22. Chen X, Huang M, Fu Y. Simultaneous acquisition of near infrared image of hand vein and pulse for liveness dorsal hand vein identification. Infrared Phys Technol. 2021 Jun 1;115:103688.

23. Ma H, Hu N, Fang C. The biometric recognition system based on near-infrared finger vein image. Infrared Phys Technol. 2021 Aug 1;116:103734.

24. Anila S, Mary A, Chacko MO. Identifying pedophiles by visualizing vein patterns from color skin images. International Journal of Computer Science and Information Technologies. 2017;8(1):45-7.

25. Ou WF, Po LM, Zhou C, Rehman YAU, Xian PF, Zhang YJ. Fusion loss and inter-class data augmentation for deep finger vein feature learning. Expert Syst Appl. 2021 Jun 1;171:114584.

26. Shakil S, Arora D, Zaidi T. An optimal method for identification of finger vein using supervised learning. Meas: Sens. 2023 Feb 1;25:100583.

27. Mohd Asaari MS, Suandi SA , Rosdi BA. Fusion of band limited phase only correlation and width centroid contour distance for finger based biometrics. Expert Syst Appl. 2014;41(7):3367-82.

28. Kumar A, Zhou Y. Human identification using finger images. IEEE Trans Image Process. 2012 Apr;21(4):2228-44.

29. Yin Y, Liu L, Sun X. SD UMLA-HMT: a multimodal biometric database. Conference: Biometric Recognition – 6th Chinese Conference, CCBR 2011, Beijing, China, December 3-4, 2011. Proceedings. Lect Notes Comput Sci. 2011;260-8.

30. Yüksel A, Akarun L, Sankur B. Biometric identification through hand vein patterns. In: 2010 International Workshop on Emerging Techniques and Challenges for Hand-Based Biometrics (ETCHB). Istanbul, Turkey: IEEE; 2010. p. 1-6.

31. Tome P, Marcel S. On the vulnerability of palm vein recognition to spoofing attacks. In: 2015 International Conference on Biometrics (ICB). Phuket, Thailand: IEEE; 2015. p. 319-25.

32. Tome P, Raghavendra R, Busch C, Tirunagari S, Poh N, Shekar BH, et al. The 1st Competition on counter measures to finger vein spoofing attacks. In: Proceedings of 2015 International Conference on Biometrics (ICB). Phuket, Thailand : IEEE; 2015. p. 513–8.

33. Yang W, Yu X, Liao Q. Personal authentication using finger vein pattern and finger-dorsa texture fusion. In: MM’09 – Proceedings of the 17th ACM International Conference on Multimedia. Association for Computing Machinery; 2009. p. 905-8.

34. Staal J, Abramoff MD, Niemeijer M, Viergever MA, van Ginneken B. Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Imaging. 2004;23(4):501-9. Available from: https://drive.grand-challenge.org/

35. Kauba C, Prommegger B, Uhl A. Focussing the beam – A new laser illumination based data set providing insights to finger-vein recognition. In: 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS ). Redondo Beach, CA: IEEE; 2018. p. 1-9.

36. Babalola FO, Bitirim Y, Toygar Ö. Palm vein recognition through fusion of texture-based and CNN -based methods. Signal Image Video P. 2021 Apr 1;15(3):459-66.

37. Kannala J, Rahtu E. BSIF: Binarized statistical image features. In: Proceedings – International Conference on Pattern Recognition (ICPR 2012). Tsukuba, Japan: IEEE; 2012. p. 1363-6.

38. Alkassar SH M. Advancing the technology of sclera recognition (Doctoral dissertation). Newcastle upon Tyne: Newcastle University; 2018.

39. Uhl A, Busch C, Marcel S, Veldhuis R, editors. Handbook of vascular biometrics. Cham: Springer Nature; 2020. p. 599.

40. He SY, Fan C-P. SIFT features and SVM learning based sclera recognition method with efficient sclera segmentation for identity identification. In: 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS ). Hsinchu, Taiwan: IEEE; 2019. p. 297-8.

41. A . Suganya and, M. Sivitha. A new biometric using sclera vein recognition for human identification. In: 2014 IEEE International Conference on Computational Intelligence and Computing Research. Coimbatore, India: IEEE; 2014. p. 1-4.

42. Kuzu RS , Maiorana E, Campisi P. Gender-specific characteristics for hand-vein biometric recognition: analysis and exploitation. IEEE Access. 2023 Jan 26;11:11700-11710.

43. Zabala-Blanco D, Hernández-García R, Barrientos RJ. SoftVein-WELM: a weighted extreme learning machine model for soft biometrics on palm vein images. Electronics. 2023 Sep 1;12(17):3608.

44. Kuzu RS , Piciucco E, Maiorana E, Campisi P. On-the-fly finger-vein-based biometric recognition using deep neural networks. IEEE Trans Inf Forensics Secur. 2020;15:2641-54.

Informacje

Informacje: Problems of Forensic Sciences (Z Zagadnień Nauk Sądowych), 2024, 139, s. 227 - 244

DOI: https://doi.org/10.4467/12307483PFS.24.016.20915

Typ artykułu: Oryginalny artykuł naukowy

Tytuły:

Angielski: Direction of vein mapping in forensic identification: a comprehensive review

Polski: Mapowanie żył w badaniach kryminalistycznych – przegląd literatury