Robust and Automatic Algorithm for Palmprint ROI Extraction

Noor Yousif - University of Technology-Iraq, Baghdad, Iraq
Samar Qassir - Mustansiriyah University, Baghdad, Iraq
Dena George - Mustansiriyah University, Baghdad, Iraq


Citation Format:



DOI: http://dx.doi.org/10.62527/joiv.9.1.2801

Abstract


The ridges, creases, wrinkles, and minutiae on the palmprint region of interest (ROI) are important features. These features are employed to confirm or identify an individual. One inevitable issue in the realization of palmprint recognition systems is the extraction procedure of this region under unrestricted environments. The variety in palm sizes, postures, lighting conditions, and backgrounds, however, certainly presents a significant issue. Finding and extracting the palm's area of interest (ROI) will be our main goal. This research introduces a robust automated algorithm based on square construction and each YCbCr color space features. After reading the image of the colored hand, this algorithm goes through two stages. Firstly, convert to the YCbCr color space. This stage guarantees precise locating of the hand region in addition to deleting irrelevant information from the image. Secondly, determining ROI is based on applying three steps: locating three key references, utilizing these key references to construct the main line, and finally, constructing the ROI square. The total color hand images (230) were used to test and evaluate the newly introduced algorithm; 30 were collected from the internet; and 200 were chosen from the Birjand University Mobile Palmprint Database (BMPD). The hand images include two orientations, left and right, varying sizes and backgrounds, uneven illumination, shadows, and some hand images have items on the finger(s). The experimental findings demonstrate that the introduced algorithm effectively attained 100% and 99.565% sensitivity and accuracy, respectively.


Keywords


Biometric; Palmprint Detection; YCbCr color space; ROI segmentation

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References


F. E. Sadeq, and Z. T. M. Al-Ta'i, “Comparison Between Face and Gait Human Recognition Using Enhanced Convolutional Neural Network”, Journal of Applied Engineering and Technological Science (JAETS), 5(1), 18-30, 2023.

A. A. Ismail, A. Babu, E. Barka, and K. Shuaib. “AI-powered biometrics for Internet of Things security: A review and future vision”, Journal of Information Security and Applications, vol. 82, pp. 103748, May 2024.

S. Abdul-Kader Hussain, H. Al-Nayyef, B. Al Kindy, and S. Amil Qassir, “Human Earprint Detection Based on Ant Colony Algorithm”, Int J Intell Syst Appl Eng, vol. 11, no. 2, pp. 513–517, Feb. 2023.J. Khodadoust, R. Monroy, M.A. M.-Pérez, O. L. González, W. Kusakunniran, A. Boller and P. Terhörst. “A novel indexing algorithm for latent palmprints leveraging minutiae and orientation field”, Intelligent Systems with Applications, vol. 21, pp. 200320, Mar. 2024.

S. Ma, Q. Hu, S. Zhao, S. Chen, and L. Jiang, “SYEnet: Simple yet effective network for palmprint recognition,” Information sciences, vol. 669, pp. 120518–120518, May 2024, doi: https://doi.org/10.1016/j.ins.2024.120518.

S. Li, L. Fei, B. Zhang, X. Ning, and L. Wu, “Hand-based multimodal biometric fusion: A review,” Information Fusion, p. 102418, Apr. 2024, doi: https://doi.org/10.1016/j.inffus.2024.102418.

H. Wang, L. Su, H. Zeng, P. Chen, R. Liang, and Y. Zhang, “Anti-spoofing study on palm biometric features,” Expert Systems with Applications, vol. 218, p. 119546, May 2023, doi: https://doi.org/10.1016/j.eswa.2023.119546.

L. Su, L. Fei, S. Zhao, J. Wen, J. Zhu, and S. Teng, “Learning modality-invariant binary descriptor for crossing palmprint to palm-vein recognition,” Pattern recognition letters, vol. 172, pp. 1–7, Aug. 2023, doi: https://doi.org/10.1016/j.patrec.2023.05.026.

Ooi Zhi Jie, Lim Tong Ming, and Tan Chi Wee, “Biometric Authentication based on Liveness Detection Using Face Landmarks and Deep Learning Model,” JOIV: International Journal on Informatics Visualization, vol. 7, no. 3–2, pp. 1057–1065, Nov. 2023, doi: https://doi.org/10.30630/joiv.7.3-2.2330.

Ganjar Gingin Tahyudin, Mahmud Dwi Sulistiyo, Muhammad Arzaki, and E. Rachmawati, “Classifying Gender Based on Face Images Using Vision Transformer,” JOIV: International Journal on Informatics Visualization, vol. 8, no. 1, pp. 18–18, Mar. 2024, doi: https://doi.org/10.62527/joiv.8.1.1923.

A. Setiawan, Riyanto Sigit, and Rika Rokhana, “Face Recognition Using Convolution Neural Network Method with Discrete Cosine Transform Image for Login System,” JOIV: International Journal on Informatics Visualization, vol. 7, no. 2, pp. 502–510, May 2023, doi: https://doi.org/10.30630/joiv.7.2.1546.

J. Wan, D. Zhong, and H. Shao, “Palmprint recognition system for mobile device based on circle loss,” Displays, vol. 73, pp. 102214–102214, Jul. 2022, doi: https://doi.org/10.1016/j.displa.2022.102214.

Feng Yulin and A. Kumar, “BEST: Building evidences from scattered templates for accurate contactless palmprint recognition,” Pattern recognition, vol. 138, pp. 109422–109422, Jun. 2023, doi: https://doi.org/10.1016/j.patcog.2023.109422.

G. Ananthi, G. Shenbagalakshmi, A.T. Anisha Shruti, and G. Sandhiya, “Authentication by Palmprint Using Difference of Block Means Code,” Advances in computational intelligence and robotics book series, pp. 185–199, Jun. 2023, doi: https://doi.org/10.4018/978-1-6684-9804-0.ch011.

J. Wan, D. Zhong, and H. Shao, “Palmprint recognition system for mobile device based on circle loss,” Displays, vol. 73, pp. 102214–102214, Jul. 2022, doi: https://doi.org/10.1016/j.displa.2022.102214.

A.-S. Ungureanu, S. Salahuddin, and P. Corcoran, “Toward Unconstrained Palmprint Recognition on Consumer Devices: A Literature Review,” IEEE Access, vol. 8, pp. 86130–86148, 2020, https://doi.org/10.1109/access.2020.2992219.

A. Kong, D. Zhang, and M. Kamel, “A survey of palmprint recognition,” Pattern Recognition, vol. 42, no. 7, pp. 1408–1418, Jul. 2009, doi: https://doi.org/10.1016/j.patcog.2009.01.018.

R. Wang, D. Ramos, J. Fierrez, and R. P. Krish, “Automatic region segmentation for high-resolution palmprint recognition: Towards forensic scenarios,” Biblos-e Archivo (Universidad Autónoma de Madrid), Oct. 2013, doi: https://doi.org/10.1109/ccst.2013.6922078.

A. S. El, H. M. Ebeid, M. Roushdy, and Z. T. Fayed, “A method for contactless palm ROI extraction,” Dec. 2016, doi: https://doi.org/10.1109/icces.2016.7821999.

S. Zhao and B. Zhang, “Robust and adaptive algorithm for hyperspectral palmprint region of interest extraction,” IET biometrics, vol. 8, no. 6, pp. 391–400, Jun. 2019, doi: https://doi.org/10.1049/iet-bmt.2018.5051.

Q. Xiao, J. Lu, W. Jia, and X. Liu, “Extracting Palmprint ROI from Whole Hand Image Using Straight Line Clusters,” IEEE Access, pp. 1–1, 2019, doi: https://doi.org/10.1109/access.2019.2918778.

K. Luo and D. Zhong, “Robust and adaptive region of interest extraction for unconstrained palmprint recognition,” Journal of electronic imaging, vol. 30, no. 03, May 2021, doi: https://doi.org/10.1117/1.jei.30.3.033005

Paulo et al., “Efficient machine learning approach for volunteer eye-blink detection in real-time using webcam,” Expert Systems With Applications, vol. 188, pp. 116073–116073, Feb. 2022, doi: https://doi.org/10.1016/j.eswa.2021.116073.

C. Lin, “Face detection in complicated backgrounds and different illumination conditions by using YCbCr color space and neural network,” Pattern Recognition Letters, vol. 28, no. 16, pp. 2190–2200, Dec. 2007, doi: https://doi.org/10.1016/j.patrec.2007.07.003

A. H. Abbas, N. M. Mirza, S. A. Qassir, and L. H. Abbas, “Maize Leaf Images Segmentation Using Color Threshold and K-means Clustering Methods to Identify the Percentage of the Affected Areas,” IOP Conference Series: Materials Science and Engineering, vol. 745, no. 1, p. 012048, Feb. 2020, doi: https://doi.org/10.1088/1757-899x/745/1/012048.

Y.-Z. Bahia, Fedila Meriem, and Bengherabi Messaoud, “Face spoofing detection using Heterogeneous Auto-Similarities of Characteristics,” Engineering applications of artificial intelligence, vol. 130, pp. 107788–107788, Apr. 2024, doi: https://doi.org/10.1016/j.engappai.2023.107788.

Arpita Panigrahi, H. Sharma, and A. Mukherjee, “Video-based HR measurement using adaptive facial regions with multiple color spaces,” Biocybernetics and Biomedical Engineering, vol. 44, no. 1, pp. 68–82, Jan. 2024, doi: https://doi.org/10.1016/j.bbe.2023.12.001.

Nouha Khediri, Mohamed Ben Ammar, and Monji Kherallah, “Comparison of Image Segmentation using Different Color Spaces,” 2021 IEEE 21st International Conference on Communication Technology (ICCT), Oct. 2021, doi: https://doi.org/10.1109/icct52962.2021.9658094

L.-C. Wang, W.-L. Song, and X. Guo, “Out-of-plane compressive mechanical properties of square-twist origami folded-stable state,” International journal of mechanical sciences, vol. 246, pp. 108104–108104, May 2023, doi: https://doi.org/10.1016/j.ijmecsci.2023.108104.

A.-P. Buta, Andrei-Marius Silaghi, Aldo De Sabata, and Ladislau Matekovits, “Multiple-Notch Frequency Selective Surface for Automotive Applications,” Jun. 2020, doi: https://doi.org/10.1109/comm48946.2020.9142001.