Arnab Mukherjee; Md. Zahidul Islam; Raju Roy; Lasker Ershad Ali

Block-based Local Binary Patterns for Distant Iris Recognition Using Various Distance Metrics

PDF (1061KB), PP.83-99

Views: 0 Downloads: 0

Author(s)

Arnab Mukherjee ¹ Md. Zahidul Islam ² Raju Roy ³ Lasker Ershad Ali ^3,*

1. Department of Mathematics, International University of Business Agriculture and Technology, Dhaka 1230, Bangladesh

2. School of Engineering, Design and Built Environment, Western Sydney University, Kingswood, NSW 2751, Australia

3. Mathematics Discipline, Khulna University, Khulna 9208, Bangladesh

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2024.03.07

Received: 2 May 2023 / Revised: 11 Jun. 2023 / Accepted: 23 Aug. 2023 / Published: 8 Jun. 2024

Index Terms

Iris Recognition, Block Descriptor, Local Binary Patterns, Distance Metrics, Confusion Matrix

Abstract

Nowadays iris recognition has become a promising biometric for human identification and authentication. In this case, feature extraction from near-infrared (NIR) iris images under less-constraint environments is rather challenging to identify an individual accurately. This paper extends a texture descriptor to represent the local spatial patterns. The iris texture is first divided into several blocks from which the shape and appearance of intrinsic iris patterns are extracted with the help of block-based Local Binary Patterns (LBPb). The concepts of uniform, rotation, and invariant patterns are employed to reduce the length of feature space. Additionally, the simplicity of the image descriptor allows for very fast feature extraction. The recognition is performed using a supervised machine learning classifier with various distance metrics in the extracted feature space as a dissimilarity measure. The proposed approach effectively deals with lighting variations, blur focuses on misaligned images and elastic deformation of iris textures. Extensive experiments are conducted on the largest and most publicly accessible CASIA-v4 distance image database. Some statistical measures are computed as performance indicators for the validation of classification outcomes. The area under the Receiver Operating Characteristic (ROC) curves is illustrated to compare the diagnostic ability of the classifier for the LBP and its extensions. The experimental results suggest that the LBPb is more effective than other rotation invariants and uniform rotation invariants in local binary patterns for distant iris recognition. The Braycurtis distance metric provides the highest possible accuracy compared to other distance metrics and competitive methods.

Cite This Paper

Arnab Mukherjee, Md. Zahidul Islam, Raju Roy, Lasker Ershad Ali, "Block-based Local Binary Patterns for Distant Iris Recognition Using Various Distance Metrics", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.16, No.3, pp. 83-99, 2024. DOI:10.5815/ijigsp.2024.03.07

Reference

[1]R. Hidayat and K. Ihsan, “Robust Feature Extraction and Iris Recognition for Biometric Personal Identification,” Biometric Systems, Design and Applications, InTech, Ch.9, pp. 149-168, 2011, doi: 10.5772/18374.
[2]R. Hentati, M. Hentati, and M. Abid, “Development a new algorithm for iris biometric recognition,” International Journal of Computer and Communication Engineering, vol. 1, no. 3, pp. 283-286, 2012.
[3]A. Das, and R. Parekh, “Iris recognition using a scalar based template in Eigen-space,” Int. Journal of Computer Science and Telecommunication, vol. 3, pp. 74-49, 2012.
[4]K. S. N. Ripon, L. E. Ali, N. Siddique, and J. Ma, “Convolutional neural network-based eye recognition from distantly acquired face images for human identification,” International Joint Conference on Neural Networks (IJCNN), pp. 1–8, 2019, doi:10.1109/IJCNN.2019. 8852190.
[5]M. Savoj, and S. A. Monadjemi, “Iris localization using circle and fuzzy circle detection method,” World Academy of Science, Engineering and Technology, vol. 6, no. 1 pp. 91-93, 2012, doi.org/10.5281/zenodo.1055250.
[6]A. S. Al-Waisy, R. Qahwaji, S. Ipson, S. Al-Fahdawi, and T. A. Nagem, “A multi-biometric iris recognition system based on a deep learning approach,” Pattern Analysis and Applications, vol. 21, no. 3, pp. 783-802, 2018, doi.org/10.1007/s10044-017-0656-1.
[7]T. Ahonen, A. Hadid, and M. Pietikäinen, “Face Recognition with Local Binary Patterns,” T. Pajdla, J. Matas (Eds.), Computer Vision -ECCV 2004, Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, vol.3021, pp. 469–481, 2004, doi.org/10.1007/978-3-540-24670-1_36
[8]V. Takala, T. Ahonen, and M. Pietikäinen, “Block-based methods for image retrieval using local binary patterns,” Image Analysis: 14th Scandinavian Conference, SCIA 2005, Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, vol.3540, pp. 882–891, 2005, doi.org/10.1007/11499145_89
[9]T. Maenpaa, and M. Pietikainen, “Texture analysis with local binary patterns,” Handbook of Pattern Recognition and Computer Vision, pp.197-216, 2005, doi: 10.1142/9789812775320_0011.
[10]S. Liao, X. Zhu, Z. Lei, L. Zhang, and S. Z. Li, “Learning Multi-scale Block Local Binary Patterns for Face Recognition,” Advances in Biometrics, ICB 2007. Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, vol. 4642, pp. 828–837, 2017, doi.org/10.1007/978-3-540-74549-5_87.
[11]T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 7, pp. 971-987, 2002, doi:10.1109/TPAMI.2002.1017623.
[12]J. Daugman, “High confidence visual recognition of persons by a test of statistical independence,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp. 1148-1161, 1993, doi:10.1109/ 34.244676.
[13]R. P. Wildes, “Iris recognition: an emerging biometric technology,” Proceedings of the IEEE, vol. 85, no. 9, pp. 1348-1363, 1997, doi: 10.1109/5.628669.
[14]W. Boles, and B. Boashash, “A human identification technique using images of the iris and wavelet transform,” IEEE Transactions on Signal Processing, vol. 46, no. 4, pp. 1185-1188, 1998, doi:10.1109/78.668573.
[15]S. Lim, K. Lee, O. Byeon, and T. Kim, “Efficient iris recognition through improvement of feature vector and classifier,” ETRI Journal, vol. 23, no. 2, pp. 61-70, 2001, doi.org/10.4218/etrij.01.0101.0203.
[16]L. Masek, “Recognition of human iris patterns for biometric identification,” Master’s Thesis, Department of Computer Science and Software Engineering, University of Western Australia, Crawley, 2003.
[17]C. Fancourt, L. Bogoni, K. Hanna, Y. Guo, R. Wildes, N. Takahashi, and U. Jain, “Iris recognition at a distance,” International Conference on Audio and Video-Based Biometric Person Authentication, Springer, vol. 3546, pp. 1–13, 2017, doi:10.1007/11527923_1.
[18]Z. Sun, T. Tan, and X. Qiu, “Graph Matching Iris Image Blocks with Local Binary Pattern,” In: Zhang, D., Jain, A.K. (eds) Advances in Biometrics, ICB 2006, Lecture Notes in Computer Science, Springer, Berlin, Heidelberg. vol 3832, pp. 366–372, 2005, doi.org/10.1007/11608288_49.
[19]Y. He, G. Feng, Y. Hou, L. Li, and E. Micheli-Tzanakou, “Iris feature extraction method based on LBP and chunked encoding,” 7th International Conference on Natural Computation, Shanghai, China, vol. 3, pp. 1663–1667, 2011, doi: 10.1109/ICNC.2011.6022302.
[20]M. Shams, M. Z. Rashad, O. Nomir, and R. El-Awady, “Iris recognition based on LBP and combined LVQ classifier,” International Journal of Computer Science & Information Technology, vol. 3, no. 1, pp. 67–78, 2011, doi:10.5121/ijcsit.2011.3506.
[21]I. Hamouchene, and S. Aouat, “A cognitive approach for texture analysis using neighbors-based binary patterns,” 13th International Conference on Cognitive Informatics and Cognitive Computing, IEEE, pp. 94–99, 2014, doi: 10.1109/ICCI-CC.2014.6921447.
[22]B. Connor, and K. Roy, “Iris recognition using level set and local binary pattern,” International Journal of Computer Theory and Engineering, vol. 6, pp. 416–420, 2014, doi:10.7763/IJCTE. 2014. V6. 901.
[23]C. Li, W. Zhou, and S. Yuan, “Iris recognition based on a novel variation of local binary pattern,” the visual computer, vol. 31, no. 10, pp. 1419–1429, 2015, doi.org/10.1007/s00371-014-1023-5.
[24]N. S. Sarode, and A. M. Patil, “Iris recognition using LBP with classifiers KNN and NB,” International Journal of Science and Research, vol. 4, no. 1, pp. 1904–1908, 2015.
[25]L. E. Ali, J. Luo, and J. Ma, “Effective iris recognition for distant images using Log Gabor wavelet based Contourlet transform features,” International Conference on Intelligent Computing, ICIC 2017, Lecture Notes in Computer Science, Springer, Cham, vol. 10361 pp. 293–303, 2017, doi.org/10.1007/978-3-319-63309-1_27.
[26]G. Huo, H. Guo, Y. Zhang, Q. Zhang, W. Li, and B. Li, “An effective feature descriptor with Gabor filter and uniform local binary pattern transcoding for iris recognition, ” Pattern Recognition and Image Analysis, vol. 29, pp. 688 – 694, 2019, doi.org/10.1134/S1054661819040059.
[27]A. A. Abdo, A. Lawgali, and A. K. Zohdy, “Iris recognition based on histogram equalization and discrete cosine transform,” Proceedings of the 6th International Conference on Engineering & MIS 2020, pp. 1–5, 2020, doi.org/10.1145/3410352.3410758
[28]P. Karn, X. He, J. Zhang, and Y. Zhang, “An experimental study of relative total variation and probabilistic collaborative representation for iris recognition,” Multimedia Tools and Applications, vol. 79, no. 43, pp. 31783–31801, 2020, doi.org/10.1007/s11042-020-09553-7.
[29]R. Agarwal, A. S. Jalal, and K. V. Arya, “Local binary hexagonal extrema pattern (LBHXEP): a new feature descriptor for fake iris detection,” The Visual Computer, Springer, vol. 79, pp. 1359–1368, 2021, doi.org/10.1007/s11277-020-07700-9.
[30]W. El-Tarhouni, A. Abdo, and A. ELmegreisi, “Feature fusion using the local binary pattern histogram Fourier and the pyramid histogram of feature fusion using the local binary pattern-oriented gradient in iris recognition,” 1st International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering MI-STA, pp. 853–857, 2021. doi:10.1109/MI-STA52233. 2021.9464473.
[31]M. A. Taha, and H. M. Ahmed, “Iris Features Extraction and Recognition based on the Local Binary Pattern Technique,” International Conference on Advanced Computer Applications (ACA), IEEE, pp. 16–21, 2021, doi: 10.1109/ACA52198.2021.9626827.
[32]P. Podder, R. H. Mondal, and J. Kamruzzaman, “Chapter 1 - iris feature extraction using three-level Haar wavelet transform and modified local binary pattern,” A. A. Elngar, R. Chowdhury, M. Elhoseny, V. E. Balas (Eds.), Applications of Computational Intelligence in Multi-Disciplinary Research, Advances in Biomedical Information, Academic Press, pp. 1–15, 2022, doi.org/10.1016/B978-0-12-823978-0.00005-8.
[33]A. Mukherjee, K. S. N. Ripon, L. E. Ali, Z. Islam, and G. Mamun Al-Imran, “Image gradient based iris recognition for distantly acquired face images using distance classifiers,” In: Gervasi, O., Murgante, B., Misra, S., Rocha, A.M.A.C., Garau, C. (eds) Computational Science and Its Applications – ICCSA 2022 Workshops, ICCSA 2022, Lecture Notes in Computer Science, Springer, Cham, vol. 13381, pp. 239–252, 2022 https://doi.org/10.1007/978-3-031-10548-7_18.
[34]H. A. Abu Alfeilat, A. B. Hassanat, O. Lasassmeh, A. S. Tarawneh, M. B. Alhasanat, H. S. Eyal Salman, and V. S. Prasath, “Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review,” Big data, vol. 7, no. 4, pp. 221–248, 2019, doi: 10.1089/big.2018.0175.
[35]C.-W. Tan, and A. Kumar, “Unified framework for automated iris segmentation using distantly acquired face images,” IEEE Transactions on Image Processing, vol. 21, no. 9, pp. 4068–4079, 2012. doi:10.1109/TIP.2012. 2199125.
[36]L. Grady, “Random walks for image segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 11, pp. 1768–1783, 2006. doi:10.1109/TPAMI.2006.233.
[37]C.-W. Tan, and A. Kumar, “Towards online iris and periocular recognition under relaxed imaging constraints,” IEEE Transactions on Image Processing, vol. 22, no. 10, pp. 3751–3765, 2013. doi:10.1109/TIP.2013.2260165.
[38]J. Daugman, “How iris recognition works,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 21–30, 2004. doi:10.1109/TCSVT.2003.818350.
[39]A. Mukherjee, M. Z. Islam, G. Mamun-Al-Imran, and L. E. Ali, “Iris Recognition Using Wavelet Features and Various Distance Based Classification,” IEEE International Conference on Electronics, Communications and Information Technology (ICECIT), Khulna, Bangladesh, pp. 1–4, 2021, doi: 10.1109/ICECIT54077.2021.9641118.
[40]T. Ojala, M. Pietikäinen, and D. Harwood, “A comparative study of texture measures with classification based on featured distributions,” Pattern recognition, vol. 29, no. 1, pp. 51–59, 1996, doi.org/10.1016/0031-3203(95)00067-4.
[41]B. Julsing, “Face recognition with local binary patterns,” Research No. SAS008-07, University of Twente, Department of Electrical Engineering, Mathematics & Computer Science (EEMCS).
[42]Biometrics ideal test casia iris image database (2011). http:// biometrics.idealtest.org/. Accessed: 28 Apr 2022.
[43]L. E. Ali, J. Luo, and J. Ma, “Iris recognition from distant images based on multiple feature descriptors and classifiers,” IEEE 13th International Conference on Signal Processing (ICSP), pp. 1357–1362, 2016, doi:10.1109/ICSP.2016.7878048.
[44]A. Kumar, and T.-S. Chan, C.-W. Tan, “Human identification from at-a-distance face images using sparse representation of local iris features,” 5th IAPR International Conference on Biometrics (ICB), pp. 303–309, 2012, doi:10.1109/ICB.2012.6199824.
[45]C.-W. Tan, and A. Kumar, “Adaptive and localized iris weight map for accurate iris recognition under less constrained environments,” IEEE 6th International Conference on Biometrics: Theory, Applications, and Systems (BTAS), pp. 1–7, 2013, doi: 10.1109/BTAS.2013.6712751.
[46]C.-W. Tan, and A. Kumar, “Accurate iris recognition at a distance using stabilized iris encoding and Zernike moments phase features,” IEEE Transactions on Image Processing, vol. 23, no. 9, pp. 3962–3964, 2014, doi: 10.1109/TIP.2014.2337714.

International Journal of Image, Graphics and Signal Processing (IJIGSP)