Morphological and Variational Analysis of Upper Limb Superficial Vein Patterns in Human Identification and Forensic Anatomy
Abstract
Keywords
Full Text:
PDFReferences
1. FBI. (n. d.). Next Generation Identification (NGI). Retrieved from https://le.fbi.gov/science-and-lab/biometrics-and-fingerprints/biometrics/next-generation-identification-ngi
2. Hemis, M., Kheddar, H., Bourouis, S., & Saleem, N. (2024). Deep learning techniques for hand vein biometrics: A comprehensive review. Information Fusion, 114, 102716. doi: 10.1016/j.inffus.2024.102716
3. Shaheed, K., Mao, A., Qureshi, I., Kumar, M., Hussain, S., & Zhang, X. (2021). Recent advancements in finger vein recognition technology: Methodology, challenges and opportunities. Information Fusion, 79, 84–109. doi: 10.1016/j.inffus.2021.10.004
4. López-González, P., Baturone, I., Hinojosa, M., & Arjona, R. (2022). Evaluation of a vein biometric recognition system on an ordinary smartphone. Applied Sciences, 12(7), 3522. doi: 10.3390/app12073522
5. Garcia-Martin, R., & Sanchez-Reillo, R. (2020). Wrist vascular biometric recognition using a portable contactless system. Sensors, 20(5), 1469. doi: 10.3390/s20051469
6. Gonzalez-Soler, L. J., Zyla, K. M., Rathgeb, C., & Fischer, D. (2024). Contactless hand biometrics for forensics: review and performance benchmark. EURASIP Journal on Image and Video Processing, 2024(1). doi: 10.1186/s13640-024-00642-3
7. Jain, A. K., & Ross, A. (2015). Bridging the gap: from biometrics to forensics. Philosophical Transactions of the Royal Society B Biological Sciences, 370(1674), 20140254. doi: 10.1098/rstb.2014.0254
8. Fiolka, J., Bernacki, K., Farah, A., & Popowicz, A. (2023). Multi-Wavelength biometric acquisition system utilising NIR imaging of finger vasculature. Sensors, 23(4), 1981. doi: 10.3390/s23041981
9. Chen, A. I., Balter, M. L., Maguire, T. J., & Yarmush, M. L. (2016). 3D near infrared and ultrasound imaging of peripheral blood vessels for Real-Time localisation and needle guidance. Lecture Notes in Computer Science, 9902, 388–396. doi: 10.1007/978-3-319-46726-9_45
10. Bacchetti, P., McCulloch, C. E., & Segal, M. R. (2008). Simple, defensible sample sizes based on cost efficiency. Biometrics, 64(2), 577–585. doi: 10.1111/j.1541-0420.2008.01004_1.x
11. Dass, S., Zhu, N. Y., & Jain, A. (2006). Validating a biometric authentication system: sample size requirements. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12), 1902–1913. doi: 10.1109/tpami.2006.255
12. Mao, L., Kim, K., & Miao, X. (2021). Sample size formula for general win ratio analysis. Biometrics, 78(3), 1257–1268. doi: 10.1111/biom.13501
13. Wang, L., & Leedham, G. (2006). Near- and Far- Infrared Imaging for Vein Pattern Biometrics. IEEE International Conference on Video and Signal Based Surveillance, 52. doi: 10.1109/avss.2006.80
14. Bookstein, F. L. (1997). Shape and the Information in Medical Images: A Decade of the Morphometric Synthesis. Computer Vision and Image Understanding, 66(2), 97–118. doi: 10.1006/cviu.1997.0607
15. Zhou, S. K., Greenspan, H., Davatzikos, C., Duncan, J. S., Van Ginneken, B., Madabhushi, A., Prince, J. L., Rueckert, D., Summers, R. M., Zhou, S. K., Greenspan, H., Davatzikos, C., Duncan, J. S., Van Ginneken, B., Madabhushi, A., Prince, J. L., Rueckert, D., & Summers, R. M. (2021). A review of deep learning in medical imaging: imaging traits, technology trends, case studies with progress highlights, and future promises. Proceedings of the IEEE, 109(5), 820–838. doi: 10.1109/jproc.2021.3054390
16. Hallgrimsson, B., Percival, C. J., Green, R., Young, N. M., Mio, W., & Marcucio, R. (2015). Morphometrics, 3D imaging, and craniofacial development. Current Topics in Developmental Biology, 115, 561–597. doi: 10.1016/bs.ctdb.2015.09.003
Article Metrics
Metrics powered by PLOS ALM
Refbacks
- There are currently no refbacks.
Copyright (c) 2025 Olasoji O. Agboola, Thomas K. Adenowo

This work is licensed under a Creative Commons Attribution 4.0 International License.



