Local Binary Pattern in the Frequency Domain: Performance Comparison with Discrete Cosine Transform and Haar Wavelet Transform
Received: 1 January 2025 | Revised: 19 January 2025 | Accepted: 29 January 2025 | Online: 22 February 2025
Corresponding author: Eren Sen
Abstract
This study presents a new method that aims to improve iris recognition performance by amplifying high-frequency components in the frequency domain, considering that iris images naturally contain high-frequency details. The Haar Wavelet Transform (HWT) and Discrete Cosine Transform (DCT) are used to enhance these components and an inverse transformation is applied to obtain iris images with more details. As input, the brightness values of the 8 neighboring pixels around each central pixel are used. These values are transformed into the frequency domain, the high-frequency band is amplified, and the data are reconstructed. Feature vectors are then generated using the Local Binary Pattern (LBP) algorithm, which is fed with the enhanced images. These feature vectors are formed using a combination of local histograms rather than a global LBP histogram, which are normalized to ensure consistency. The generated feature vectors are divided into a 70% training set and a 30% test set and are tested using K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Random Forest (RF) algorithms. The proposed method provides a significant performance improvement in terms of accuracy compared to traditional approaches. While both HWT and DCT yield similar results, it has been observed that HWT is much faster. In this study, a comparison is made in terms of both speed and accuracy. Two different public iris datasets, MMU1 and MMU2, are used. This work not only introduces an innovative approach to iris recognition, but also makes a significant contribution to the manipulation of pixel brightness values in the frequency domain, with the findings being expected to guide future research.
Keywords:
iris recognition, frequency domain, high-frequency amplification, Haar Wavelet Transform (HWT), Discrete Cosine Transform (DCT), Local Binary Pattern (LBP)Downloads
References
B. O. Connor and K. Roy, "Iris Recognition Using Level Set and Local Binary Pattern," International Journal of Computer Theory and Engineering, vol. 6, no. 5, pp. 416–420, Oct. 2014.
A. Sharma and M. R. Gupta, "Iris Recognition Based Learning`Vector Quantization and Local Binary Patterns on Iris Matching," International Journal of Technical Research and Applications, vol. 3, no. 5, pp. 7–14, Sep.-Oct. 2015.
W. Li, C. Chen, H. Su, and Q. Du, "Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification," IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 7, pp. 3681–3693, Jul. 2015.
C.-H. Chan, J. Kittler, and K. Messer, "Multi-scale Local Binary Pattern Histograms for Face Recognition," in 2007 International Conference on Biometrics, Seoul, Korea, 2007, pp. 809–818.
R. Y. Lad, S. Mapari, and F. N. Sibai, "A Novel Approach to Image Classification for Detecting Abnormalities in Neuroimages based on the Structural Similarity Index Measure," Engineering, Technology & Applied Science Research, vol. 14, no. 5, pp. 17382–17387, Oct. 2024.
X. Qian, X.-S. Hua, P. Chen, and L. Ke, "PLBP: An effective local binary patterns texture descriptor with pyramid representation," Pattern Recognition, vol. 44, no. 10–11, pp. 2502–2515, Oct.-Nov. 2011.
D. Huang, C. Shan, M. Ardabilian, Y. Wang, and L. Chen, "Local Binary Patterns and Its Application to Facial Image Analysis: A Survey," IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 41, no. 6, pp. 765–781, Nov. 2011.
Y. Hong, C. Leng, X. Zhang, Z. Pei, I. Cheng, and A. Basu, "HOLBP: Remote Sensing Image Registration Based on Histogram of Oriented Local Binary Pattern Descriptor," Remote Sensing, vol. 13, no. 12, Jun. 2021, Art. no. 2328.
Y. Kaya, Ö. F. Ertuğrul, and R. Tekin, "Two novel local binary pattern descriptors for texture analysis," Applied Soft Computing, vol. 34, pp. 728–735, Sep. 2015.
D. G. R. Kola and S. K. Samayamantula, "A novel approach for facial expression recognition using local binary pattern with adaptive window," Multimedia Tools Appl., vol. 80, no. 2, pp. 2243–2262, Jan. 2021.
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, Oct. 2015.
L. Nanni, A. Lumini, and S. Brahnam, "Local binary patterns variants as texture descriptors for medical image analysis," Artificial Intelligence in Medicine, vol. 49, no. 2, pp. 117–125, Jun. 2010.
Z. Pan, S. Hu, X. Wu, and P. Wang, "Adaptive center pixel selection strategy in Local Binary Pattern for texture classification," Expert Systems with Applications, vol. 180, Oct. 2021, Art. no. 115123.
J. Ren, X. Jiang, and J. Yuan, "Noise-Resistant Local Binary Pattern With an Embedded Error-Correction Mechanism," IEEE Transactions on Image Processing, vol. 22, no. 10, pp. 4049–4060, Oct. 2013.
Z. Pan, Z. Li, H. Fan, and X. Wu, "Feature based local binary pattern for rotation invariant texture classification," Expert Systems with Applications, vol. 88, pp. 238–248, Dec. 2017.
T. Ahonen, J. Matas, C. He, and M. Pietikäinen, "Rotation Invariant Image Description with Local Binary Pattern Histogram Fourier Features," in 16th Scandinavian Conference on Image Analysis, Oslo, Norway, 2009, pp. 61–70.
X. Qi, R. Xiao, C.-G. Li, Y. Qiao, J. Guo, and X. Tang, "Pairwise Rotation Invariant Co-Occurrence Local Binary Pattern," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 11, pp. 2199–2213, Nov. 2014.
A. Nigam, V. Krishna, A. Bendale, and P. Gupta, "Iris recognition using block local binary patterns and relational measures," in IEEE International Joint Conference on Biometrics, Clearwater, FL, USA, 2014, pp. 1–6.
G. Zhao and M. Pietikainen, "Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 6, pp. 915–928, Jun. 2007.
Y. Liu, K. Xu, and J. Xu, "An Improved MB-LBP Defect Recognition Approach for the Surface of Steel Plates," Applied Sciences, vol. 9, no. 20, Oct. 2019, Art. no. 4222.
"MMU1 Iris Dataset." Kaggle, [Online]. Available: https://www.kaggle.com/datasets/naureenmohammad/mmu-iris-dataset.
"MMU2 Iris Dataset." Advanced Source Code, [Online]. Available: http://www.advancedsourcecode.com/irisdatabase.asp.
Downloads
How to Cite
License
Copyright (c) 2025 Eren Sen, Ibrahim Furkan Ince, Ali Ozkurt, Furkan Ilker Akin
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
