Dorsal Hand Vein Image Recognition: A Review

Authors

  • Department of Biology Science, College of Education for Pure Sciences /Ibn AL-Haitham, University of Baghdad, Iraq
  • Imam Ja,afar AI-Sadiq University, Nasiriyah, Iraq

DOI:

https://doi.org/10.32792/jeps.v12i2.220

Keywords:

biometrics, CNN, deep learning, VGG Net-16

Abstract

Subcutaneous vascularization has become a new solution for identification management over the past
few years. Systems based on dorsal hand veins are particularly promising for high-security settings. The
dorsal hand vein recognition system comprises the following steps: acquiring images from the database
and preprocessing them, locating the region of interest, and extracting and recognizing information from
the dorsal hand vein pattern. This paper reviewed several techniques for obtaining the dorsal hand vein
area and identifying a person. Therefore, this study just provides a comprehensive review of existing
previous theories. This model aims to offer the improvement in the accuracy rate of the system that was
shown in previous studies and to evaluate test samples and training samples for person identification
using distance criteria or neural networks.

References

M. Rajalakshmi, V. Ganapathy, and R. Rengaraj, "Palm-dorsal vein pattern authentication using

convoluted neural network (CNN)," Int J Pure Appl Math, vol. 116, no. 23, pp. 525-532, 2017.

S. W. Chin, K. G. Tay, C. C. Chew, A. Huong, and R. A. Rahim, "Dorsal hand vein authentication

system using artificial neural network," Indonesian Journal of Electrical Engineering and Computer

Science, vol. 21, no. 3, pp. 1837-1846, 2021.

S. H. Benziane and A. Benyettou, "Anisotropic diffusion filter for dorsal hand vein features

extraction," International Journal of Biology and Biomedicine, vol. 1, pp. 27-31, 2016.

J.-C. Lee, "Dorsal hand vein recognition based on EP-tree," in 2015 14th IAPR International

Conference on Machine Vision Applications (MVA), 2015, pp. 402-405: IEEE.

A. Singh, H. Goyal, and A. K. G. Amar, "Human identification based on hand dorsal vein pattern

using BRISK and SURF algorithm," International Journal of Engineering and Advanced Technology

(IJEAT), vol. 9, no. 4, pp. 2168-2175, 2020.

N. Charaya and P. Singh, "Human Authentication Based On Dorsal Hand Veins: A Review," vol,

vol. 119, pp. 2175-2185, 2015.

D. Huang, Y. Tang, Y. Wang, L. Chen, and Y. Wang, "Hand-dorsa vein recognition by matching

local features of multisource keypoints," IEEE transactions on cybernetics, vol. 45, no. 9, pp. 1823-1837,

M. H. M. Khan and N. A. M. Khan, "A new method to extract dorsal hand vein pattern using

quadratic inference function," arXiv preprint arXiv:1001.1966, 2009.

B. Sontakke, V. Humbe, and P. Yannawar, "Dorsal hand vein authentication system: a review,"

Journal of Scientific Research and Development, vol. 6, no. 5, pp. 511-514, 2017.

N. A. Al-johania and L. A. Elrefaei, "Dorsal hand vein recognition by convolutional neural

networks: feature learning and transfer learning approaches," International Journal of Intelligent

Engineering and Systems, vol. 12, no. 3, pp. 178-91, 2019.

P. Ramsoful and M. H.-M. Khan, "Feature extraction techniques for dorsal hand vein pattern," in

Third International Conference on Innovative Computing Technology (INTECH 2013), 2013, pp. 49-53:

IEEE, 2013.

K. Premalatha and A. Natarajan, "A dorsal hand vein recognition-based on local gabor phase

quantization with whitening transformation," Defence Science Journal, vol. 64, no. 2, p. 159, 2014.

J.-C. Lee, T.-M. Lo, and C.-P. Chang, "Dorsal hand vein recognition based on directional filter

bank," Signal, Image and Video Processing, vol. 10, no. 1, pp. 145-152, 2016.

K. Premalatha and A. Natarajan, "Hand vein pattern recognition using natural image statistics,"

Defence Science Journal, vol. 65, no. 2, p. 150, 2015.

Y. Wang and S. Dong, "Dorsal Hand Vein Recognition Based on Improved Bag of Visual Words

Model," in Chinese Conference on Biometric Recognition, 2017, pp. 203-212: Springer

A. H. H. Alasadi and M. H. Dawood, "Dorsal Hand-vein Images Recognition System based on

Grey Level Co-occurrence Matrix and Tamura," Int. J. Applied Pattern Recognition, vol. 4, no. 3, p. 207,

B. Belean, M. Streza, S. Crisan, and S. Emerich, "Dorsal hand vein pattern analysis and neural

networks for biometric authentication," Studies in Informatics and Control, vol. 26, no. 3, pp. 305-314,

J. Yan, L. Chong, and T. Li, "A method of dorsal hand vein identification," in Tenth International

Conference on Digital Image Processing (ICDIP 2018), 2018, vol. 10806, pp. 1578-1583: SPIE.

F. J. Pontoh, J. Y. Sari, A. A. Ilham, and I. Nurtanio, "Multispectral dorsal hand vein recognition

based on local line binary pattern," Jurnal Ilmu Komputer dan Informasi, vol. 11, no. 2, pp. 95-102, 2018.

A. Oueslati, N. Feddaoui, and K. Hamrouni, "A Human Identification Technique Through Dorsal

Hand Vein Texture Analysis Based on NSCT Decomposition," in International Conference on Soft

Computing and Pattern Recognition, 2018, pp. 183-193: Springer.

P. Arora, S. Srivastava, M. Hanmandlu, and S. Bhargava, "Robust authentication using dorsal

hand vein images," IEEE Intelligent Systems, vol. 34, no. 2, pp. 25-35, 2018.

C. Premavathi and P. Thangaraj, "Efficient Hand-dorsa Vein Pattern Recognition using KNN

classification with Completed histogram CB in TP Feature Descriptor," International Journal of Recent

Technology and Engineering (IJRTE), vol. 7, no. 4S, pp. 50-55, 2018.

Y. Wang, H. Cao, X. Jiang, and Y. Tang, "Recognition of dorsal hand vein based bit planes and

block mutual information," Sensors, vol. 19, no. 17, p. 3718, 2019.

W. Nie, B. Zhang, and S. Zhao, "A novel hyperspectral based dorsal hand recognition system," in

Proceedings of the 2019 11th International Conference on Machine Learning and Computing, 2019, pp.

-367.

N. Zulpe and B. Sontakke, "A Dorsal Hand Vein Pattern Recognition using Invariant Moment,"

International Journal of Computer Sciences and Engineering, vol.7, no. 3, 2019, pp.563-566.

P. Arora, G. Chaudhary, and S. Srivastava, "Exploiting Oriented Gradient Histogram for Dorsal

Vein Recognition," in 2019 Twelfth International Conference on Contemporary Computing (IC3), 2019,

pp. 1-4: IEEE.

S. Lefkovits, L. Lefkovits, and L. Szilágyi, "CNN approaches for dorsal hand vein based

identification," Computer Science Research Notes, 2019.

K. Vairavel, R. Nevetha, and S. Mekala, "Certain investigation on feature extraction in dorsal

hand vein image," in IOP Conference Series: Materials Science and Engineering, 2020, vol. 764, no. 1,

p. 012039: IOP Publishing.

S. W. Chin, K. G. Tay, A. Huong, and C. C. Chew, "Dorsal Hand Vein Pattern Recognition Using

Statistical Features and Artificial Neural Networks," in 2020 IEEE Student Conference on Research and

Development (SCOReD), 2020, pp. 217-221: IEEE.

K. Nadiya and V. P. Gopi, "Dorsal Hand Vein Biometric Recognition Based on Orientation of

Local Binary Pattern," in 2020 IEEE-HYDCON, 2020, pp. 1-6: IEEE

K. Alashik, S. Hussin, R. YILDIRIM, and A. ALGUTTAR, "Dorsal Hand Vein Identification

Based on Deep Convolutional Neural Networks and Visualizing Intermediate Layer Activations," Avrupa

Bilim ve Teknoloji Dergisi, pp. 512-521, 2020.

M. I. Sayed, M. Taha, and H. H. Zayed, "Real-Time Dorsal Hand Recognition Based on

Smartphone," IEEE Access, vol. 9, pp. 151118-151128, 2021.

A. Nozaripour and H. Soltanizadeh, "Robust vein recognition against rotation using kernel sparse

representation," Journal of AI and Data Mining, vol. 9, no. 4, pp. 571-582, 2021.

J. Y. Sari, S. Bantun, and Z. Noorhasanah, "Local Binary Patterns for Dorsal Hand Vein

Recognition," Proceeding KONIK (Konferensi Nasional Ilmu Komputer), vol. 5, pp. 235-239, 2021.

H. S. Hasan and M. A. Al-Sharqi, "Hand vein recognition with rotation feature matching based on

fuzzy algorithm," International Journal of Nonlinear Analysis and Applications, vol. 12, pp. 951-958,

K. M. Alashik and R. Yildirim, "Human identity verification from biometric dorsal hand vein

images using the DL-GAN method," IEEE Access, vol. 9, pp. 74194-74208, 2021.

R. Kumar, R. C. Singh, and S. Kant, "Dorsal hand vein-biometric recognition using convolution

neural network," in International Conference on Innovative Computing and Communications, 2021, pp.

-1107: Springer.

M. Z. NAYEBİ and Z. TURGUT, "Dorsal Hand Veins Based Biometric Identification System

Using Deep Learning," Erzincan University Journal of Science and Technology, vol. 14, no. 1, pp. 1-15.

M. Mohaghegh and A. Payne, "Automated biometric identification using dorsal hand images and

convolutional neural networks," in Journal of Physics: Conference Series, 2021, vol. 1880, no. 1, p.

: IOP Publishing.

J. Li, K. Li, G. Zhang, J. Wang, K. Li, and Y. Yang, "Recognition of Dorsal Hand Vein in Small-

Scale Sample Database Based on Fusion of ResNet and HOG Feature," Electronics, vol. 11, no. 17, p.

, 2022.

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Published

2023-02-19

Issue

Vol. 12 No. 2 (2022): University of Thi-Qar Journal of education for pure science

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