Finger knuckle recognition, a review on prospects and challenges based on PolyU dataset
DOI:
https://doi.org/10.29304/jqcm.2022.14.4.1087Keywords:
Finger knuckle(FK), Finger knuckle external surface, Biometrics, recognition, Identification systemAbstract
In the previous few years, Finger knuckle (Fk) has received a lot of interest as a biometric trait in recent years. It will provide economic human identification performance due to its distinct difference between human-specific alternatives of visible lines, wrinkles, and ridges spread on the surface external of all finger knuckles. The foundation for most biometric systems is Fks. This report presents a thorough analysis of the pertinent Finger knuckle investigations. The foundation for most biometric systems is Fks. The identification system through finger knuckles usually contents of 4 steps, specifically image Acquisition, image preprocessing, feature extraction, and have matched. There are numerous methods used during this research at each level. The paper is likely to highlight these methods used in the PolyU database
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References
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[2] C. H. Man, “CONTACTLESS 3D FINGER KNUCKLE IDENTIFICATION,” 2021.
[3] R. R. Jha, D. Thapar, S. M. Patil, and A. Nigam, “UBSegNet: Unified Biometric Region of Interest Segmentation Network,” Sep. 2017, doi: 10.1109/ACPR.2017.148.
[4] R. J. Woodham, “Photometric method for determining surface orientation from multiple images.” [Online]. Available: https://www.spiedigitallibrary.org/terms-of-use.
[5] K. H. M. Cheng and A. Kumar, “Advancing Surface Feature Encoding and Matching for More Accurate 3D Biometric Recognition.”
[6] N. E. Chalabi, A. Attia, and A. Bouziane, “MULTIMODAL FINGER DORSAL KNUCKLE MAJOR AND MINOR PRINT RECOGNITION SYSTEM BASED ON PCANET DEEP LEARNING,” ICTACT J. IMAGE VIDEO Process., no. 10, p. 3, 2020, doi: 10.21917/ijivp.2020.0308.
[7] M. Chaa, “LOG-GABOR BINARIZED STATISTICAL DESCRIPTOR FOR FINGER KNUCKLE PRINT RECOGNITION SYSTEM,” ICTACT J. IMAGE VIDEO Process., no. 10, p. 4, 2020, doi: 10.21917/ijivp.2020.0310.
[8] R. Hammouche, A. Attia, and S. Akrouf, “A NOVEL SYSTEM BASED ON PHASE CONGRUENCY AND GABOR-FILTER BANK FOR FINGER KNUCKLE PATTERN AUTHENTICATION,” ICTACT J. IMAGE VIDEO Process., no. 10, p. 3, 2020, doi: 10.21917/ijivp.2020.0303.
[9] M. Emadi, “Improving Human’s Finger Knuckle Identification using High Order Zernike Moments,” 2020.
[10] G. Jaswal, A. Nigam, and R. Nath, “DeepKnuckle: revealing the human identity,” Multimed. Tools Appl., vol. 76, no. 18, pp. 18955–18984, Sep. 2017, doi: 10.1007/s11042-017-4475-6.
[11] G. S. Badrinath, A. Nigam, and P. Gupta, “LNCS 7043 - An Efficient Finger-Knuckle-Print Based Recognition System Fusing SIFT and SURF Matching Scores.”
[12] A. Morales, C. M. Travieso, M. A. Ferrer, and J. B. Alonso, “Improved finger-knuckle-print authentication based on orientation enhancement,” Electron. Lett., vol. 47, no. 6, pp. 380–382, Mar. 2011, doi: 10.1049/el.2011.0156.
[13] L. Zhang, L. Zhang, and D. Zhang, “Finger-knuckle-print: a new biometric identifier,” in 2009 16th IEEE international conference on image processing (ICIP), 2009, pp. 1981–1984.
[14] L. Zhang, L. Zhang, D. Zhang, and H. Zhu, “Ensemble of local and global information for fingerknuckle-print recognition,” in Pattern Recognition, Sep. 2011, vol. 44, no. 9, pp. 1990–1998, doi: 10.1016/j.patcog.2010.06.007.
[15] K. H. M. Cheng and A. Kumar, “Accurate 3D Finger Knuckle Recognition Using Auto-Generated Similarity Functions,” IEEE Trans. Biometrics, Behav. Identity Sci., vol. 3, no. 2, pp. 203–213, Apr. 2021, doi: 10.1109/TBIOM.2021.3051062.
[16] A. Attia, M. Chaa, Z. Akhtar, and Y. Chahir, “Finger kunckcle patterns based person recognition via bank of multi-scale binarized statistical texture features,” Evol. Syst., vol. 11, no. 4, pp. 625–635, Dec. 2020, doi: 10.1007/s12530-018-9260-x.
[17] A. K. Jain, A. Ross, and S. Prabhakar, “An Introduction to Biometric Recognition,” IEEE Trans. Circuits Syst. Video Technol., vol. 14, no. 1, pp. 4–20, Jan. 2004, doi: 10.1109/TCSVT.2003.818349.
[18] R. Chlaoua, A. Meraoumia, K. E. Aiadi, and M. Korichi, “Deep learning for finger-knuckle-print identification system based on PCANet and SVM classifier,” Evol. Syst., vol. 10, no. 2, pp. 261–272, Jun. 2019, doi: 10.1007/s12530-018-9227-y.
[19] K. H. M. Cheng and A. Kumar, “Efficient and Accurate 3D Finger Knuckle Matching Using Surface Key Points,” IEEE Trans. Image Process., vol. 29, pp. 8903–8915, 2020.
[20] A. Attia, Z. Akhtar, Y. Chahir, and · Youssef Chahir, “Feature-level fusion of major and minor dorsal finger knuckle patterns for person authentication,” doi: 10.1007/s11760-020-01806-0ï.
[21] N. Lalithamani, R. Balaji, M. Ramya, S. Sruthi, and A. Aiswarya, “Finger Knuckle biometric authentication using convolution neural network,” Int. J. Pure Appl. Math, vol. 117, no. 10, pp. 31–35, 2017.
[22] Y. Zhai et al., “A novel finger-knuckle-print recognition based on batch-normalized CNN,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, vol. 10996 LNCS, pp. 11–21, doi: 10.1007/978-3-319-97909-0_2.
[23] J. C. Joshi, S. A. Nangia, K. Tiwari, and K. K. Gupta, “Finger Knuckleprint Based Personal Authentication using Siamese Network.”
[24] D. Thapar, G. Jaswal, and A. Nigam, “FKIMNet: A Finger Dorsal Image Matching Network Comparing Component (Major, Minor and Nail) Matching with Holistic (Finger Dorsal) Matching,” Apr. 2019, doi: 10.1109/IJCNN.2019.8852390.
[25] K. H. M. Cheng and A. Kumar, “Deep feature collaboration for challenging 3D finger knuckle identification,” IEEE Trans. Inf. Forensics Secur., vol. 16, pp. 1158–1173, 2020.
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Published
2022-12-02
How to Cite
AL-Janabi, D. H., & AL-Juboori, A. M. (2022). Finger knuckle recognition, a review on prospects and challenges based on PolyU dataset. Journal of Al-Qadisiyah for Computer Science and Mathematics, 14(4), Comp Page 66–73. https://doi.org/10.29304/jqcm.2022.14.4.1087
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Computer Articles
