MODERN AVAILABLE PALMPRINT DATABASES: A REVIEW
Keywords:
Biometric, palmprint, database, hyperspectral, multispectral
Abstract
The palm print is a unique and very useful biometric. A lot of research has been done on this
topic over the past few decades. Various algorithms and systems have been developed and successfully
implemented. Since this method does not provide more advanced information for personality
recognition, multispectral or hyperspectral imaging and handprint recognition could be a
potential answer to these systems. Biometric technologies have been widely used in the security
industry for authentication and identification over the past few years. An improved recognition
system is required to improve accuracy and speed. This article reviews some modern handprint
databases and describes the methods used and their accuracy. Face, fingerprint, iris, palm print,
hands are physiological biometric data. Of all biometrics, physiological biometrics offers the most
benefits. The PolyU-IITD non-contact palm image database compiled with a handheld camera
includes residents of India and China. The database of IIT Touchless Palmprints is sourced from
Delhi India students and teachers and consists of complete hand images. The database of
hyperspectral fingerprints created by the Hong Kong Polytechnic University was collected in the Biometric Research Laboratory Department using Meadowlark liquid crystal filters. The Multispectral
Fingerprint Database, Hyperspectral Database was compiled by Chinese research teams
of scientists. The polyU fingerprint database was collected from 193 people and contains
386 palms. The Chinese Academy of Sciences has developed the CASIA handprint database with
its own handprint recognition device. The XJTU fingerprint database is collected using iPhone 6S,
HUAWEI mate8, LG G4, Samsung Galaxy Note5 and MI8 gadgets. A literature review of current
research in this area is also presented. The advantages of hyperspectral images compared to multispectral
images are noted, hyperspectral images of palm prints are very difficult to fake.
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(CSIC 2018) 2018, pp. 82-88.
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identification, International Journal of Information and Communication Technology,
2019;14 (1), pp. 89-109.
34. Khandizod A.G., Deshmukh R.R. Analysis and Feature Extraction using Wavelet based Image
Fusion for Multispectral Palmprint Recognition, International J Enhanced Research in Management
& Computer Applications, ISSN. 2014:2319, pp.74-71.
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A review, ISPRS Journal of Photogrammetry and Remote Sensing, 2019 Dec 1158, pp. 279-307.
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Academic Press, 2017.
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hyperspectral images, In Hyperspectral Remote Sensing, 2020 Jan 1, pp. 167-196. Elsevier.
39. Khandizod A.G., Deshmukh R.R. Hyperspectral palmprint recognition: a review. In International
Conference on Recent Trends and Challenges in Science and Technology (RTCST
2014), Organized by Pravara Rural Education Society, Padmashri Vikhe Patil College of Arts,
Science, and Commerce, Pravaranagar 2014 Aug., pp. 20-23.
40. Thamri E., Aloui K., Naceur M.S. Selection of hyperspectral bands by adopting a dimension
reduction strategy for recognition of multispectral palmprint, In 2017 International Conference
on Advanced Systems and Electric Technologies (IC_ASET) 2017 Jan 14, pp. 91-96. IEEE.
41. Jia S., Jiang S., Lin Z., Li N., Xu M., Yu S. A survey: Deep learning for hyperspectral image
classification with few labeled samples, Neurocomputing. 2021 Aug 11448, pp. 179-204.
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and current trends. Elsevier; 2014 Jan 31, pp. 70-83.
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International Journal of Innovative Research in Computer and Communication Engineering,
Vol. 1, Issue 4, June 2013 ISSN (Print): 2320 – 9798 ISSN (Online), pp. 220-239.
Journal of Engineering Research & Technology (IJERT), ISSN, 2014, pp. 37-45.
2. Gautam K.A., Abbasov I..B., Namrta M.C. Development of non-imaging palmprint spectral
library via asd field spec4 spectroradiometer, Izvestiya SFedU. Engineering Sciences, 2019,
No. 3 (205), pp. 62-71.
3. Shao H., Zhong D., Du X. Towards efficient unconstrained palmprint recognition via deep
distillation hashing. arXiv preprint arXiv:2004.03303. 2020 Apr 7.
4. Nirmal Joshua R., 2Mr.C.Nelson kenedy babu. Palm Print Recognition Using Artificial Neural
Networks, 2020, Vol. 7, Issue14. ISSN- 2394-5125.
5. Prasanna, Yerra Satya Dhana Lakshmi, and R. Mani Deepika. Palm Print Recognition Using
Inner Finger Deep Learning Using Neural Network, International Journal, 2020, 5.12.
6. Brown D., Bradshaw K. Deep Palmprint Recognition with Alignment and Augmentation of
Limited Training Samples, SN Computer Science, 2022 Jan, 3 (1), pp. 1-7.
7. Gumaei A., Sammouda R., Al-Salman A.M., Alsanad A. An effective palmprint recognition
approach for visible and multispectral sensor images, Sensors. 2018 May, 18 (5), pp. 1575.
8. Available at: https://www4.comp.polyu.edu.hk/~csajaykr/palmprint3.htm.
9. Available at: http://www4.comp.polyu.edu.hk/~csajaykr/IITD/Database_Palm.htm.
10. Department of Computing, the Hong Kong Polytechnic University (PolyU), Hyperspectral
Palmprint database, PolyU, accessed on Aug. 22, 2013. Available at: http://www4.comp.polyu.
edu.hk/ ~biometrics/Hyperspectral Palmprint /HSP .htm. K. Elissa, “Title of paper if known,”
unpublished.
11. Zhang D., Guo Z., Gong Y. Feature Band Selection for Online Multispectral Palmprint Recognition,
In Multispectral Biometrics, Springer, Cham, 2016, pp. 153-162.
12. Khandizod, Anita & Deshmukh, Ratnadeep. Hyperspectral Palmprint Recognition System
using Phase Congurency and KNN Classifier, 2018.
13. Zhang D., Guo Z., Lu G., Zhang L, Zuo W. An online system of multispectral palmprint verification,
IEEE transactions on instrumentation and measurement, 2009 Oct 16,59 (2), pp. 480-90.
14. Zhao S., Nie W., Zhang B. Multi-feature fusion using collaborative residual for hyperspectral
palmprint recognition, In 2018 IEEE 4th International Conference on Computer and Communications
(ICCC) 2018 Dec 7, pp. 1402-1406. IEEE.
15. Nie W., Zhang B., Zhao S. Discriminative local feature for hyperspectral hand biometrics by
adjusting image acutance, Applied Sciences, 2019 Jan;9(19), pp. 41-58.
16. Akila P., Gayathri R., Meenatchi R., Ramya M., Dharini D. Analysis of Multibiometric
Palmprint Recognition System for Authentication, International Journal Of Engineering Research
& Technology (IJERT) NCICCT – 2016, 2016, Vol. 4, Issue 19.
17. Kadhm M.S., Ayad H., Mohammed M.J. Palmprint Recognition System Based On Proposed
Features Extraction And (C5. 0) Decision Tree, K-Nearest Neighbour (Knn) Classification
Approaches, J. Eng. Sci. Technol., 2021 Feb;16(1), pp. 816-31.
18. Available at: http://biometrics.idealtest.org/dbDetailForUser.do?id=5 CASIA Palmprint Database
(accessed: 29 May 2020).
19. Available at: https://www.coep.org.in/resources/coeppalmprintdatabase.
20. Shao H., Zhong D., Du X. Efficient deep palmprint recognition via distilled hashing coding,
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
Workshops, 2019, pp. 0-0.
21. Bala S. Comparative analysis of palm print recognition system with Repeated Line Tracking
method, Procedia Computer Science, 2016 Jan 1;92, pp. 578-82.
22. Khan Z., Shafait F., Hu Y., Mian A. Multispectral palmprint encoding and recognition. arXiv
preprint arXiv:1402.2941. 2014 Feb 6.
23. Li H, Zhang Z. Research on palmprint identification method based on quantum algorithms,
The Scientific World Journal, 2014 Jan 1;2014.
24. Sun J., Abdulla W., Wang W., Wang Q., Zhang H. Band Selection for Palmprint Recognition,
Journal of Advances in Information Technology, Vol. 2016 Nov. 7(4), pp. 91-98.
25. Guo J., Liu Y., Yuan W. Palmprint recognition based on phase congruency and twodimensional
principal component analysis, In 2011 4th International Congress on Image and
Signal Processing 2011 Oct 15, Vol. 3, pp. 1527-1530. IEEE.
26. Lin S., Bai Y., Tang Y. Design of online non-contact palmprint recognition simulation system,.
In 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering
and Informatics (CISP-BMEI) 2016 Oct 15, pp. 686-690. IEEE.
27. Khandizod A.G., Deshmukh R.R. Comparative analysis of image enhancement technique for
hyperspectral palmprint images, International Journal of Computer Applications, 2015 Jul;
121(23), pp. 30-55.
28. Kumar A., Shekhar S. Palmprint recognition using rank level fusion, In 2010 IEEE International
Conference on Image Processing 2010 Sep 26, pp. 3121-3124. IEEE.
29. Xu X., Guo Z., Song C., Li Y. Multispectral palmprint recognition using a quaternion matrix,
Sensors, 2012 Apr;12 (4), pp. 4633-47.
30. Zhong D., Yang Y., Du X. Palmprint recognition using siamese network, In Chinese conference
on biometric recognition, 2018 Aug 11, pp. 48-55. Springer, Cham.
31. Midhuna Naveen, Neethumol K J, Pritty Mary Mathew, Sherin Joseph, Sujitha M., Machine
Learning Algorithms based Palmprint Biometric Identification, International Journal Of Engineering
Research & Technology (IJERT) ICCIDT – 2021, Vol. 09, Issue 07.
32. Dong X., Mei L., Zhang J. Palmprint recognition based on deep convolutional neural networks,
In 2018 2nd International Conference on Computer Science and Intelligent Communication
(CSIC 2018) 2018, pp. 82-88.
33. Meraoumia A., Bendjenna H., Chitroub S. Towards a robust palmprint representation for person
identification, International Journal of Information and Communication Technology,
2019;14 (1), pp. 89-109.
34. Khandizod A.G., Deshmukh R.R. Analysis and Feature Extraction using Wavelet based Image
Fusion for Multispectral Palmprint Recognition, International J Enhanced Research in Management
& Computer Applications, ISSN. 2014:2319, pp.74-71.
35. Poonia P., Ajmera P.K., Shende V. Palmprint recognition using robust template matching.
Procedia Computer Science, 2020 Jan 1167, pp. 727-736.
36. Paoletti M.E., Haut J.M., Plaza J., Plaza A. Deep learning classifiers for hyperspectral imaging:
A review, ISPRS Journal of Photogrammetry and Remote Sensing, 2019 Dec 1158, pp. 279-307.
37. Schneider, Armin, and Hubertus Feussner. Biomedical engineering in gastrointestinal surgery.
Academic Press, 2017.
38. Garg P.K. Effect of contamination and adjacency factors on snow using spectroradiometer and
hyperspectral images, In Hyperspectral Remote Sensing, 2020 Jan 1, pp. 167-196. Elsevier.
39. Khandizod A.G., Deshmukh R.R. Hyperspectral palmprint recognition: a review. In International
Conference on Recent Trends and Challenges in Science and Technology (RTCST
2014), Organized by Pravara Rural Education Society, Padmashri Vikhe Patil College of Arts,
Science, and Commerce, Pravaranagar 2014 Aug., pp. 20-23.
40. Thamri E., Aloui K., Naceur M.S. Selection of hyperspectral bands by adopting a dimension
reduction strategy for recognition of multispectral palmprint, In 2017 International Conference
on Advanced Systems and Electric Technologies (IC_ASET) 2017 Jan 14, pp. 91-96. IEEE.
41. Jia S., Jiang S., Lin Z., Li N., Xu M., Yu S. A survey: Deep learning for hyperspectral image
classification with few labeled samples, Neurocomputing. 2021 Aug 11448, pp. 179-204.
42. Reddy P.L., Pawar S. Multispectral image denoising methods: A literature review. Materials
Today: Proceedings. 2020 Jan 1;33:4666-70.Lewis JA. Forensic document examination: fundamentals
and current trends. Elsevier; 2014 Jan 31, pp. 70-83.
43. Available at: https://en.wikipedia.org/wiki/Multispectral_image.
44. Mrs.Maheswari.M1, Ancy.S2, Dr. G.R. Suresh. Survey On Multispectral Biometric Images,
International Journal of Innovative Research in Computer and Communication Engineering,
Vol. 1, Issue 4, June 2013 ISSN (Print): 2320 – 9798 ISSN (Online), pp. 220-239.
Published
2022-08-09
Issue
Section
SECTION I. COMPUTING AND INFORMATION CONTROL SYSTEMS
