Novel Fuzzy Kernels Based Local Binary Pattern And Local Graph Structure Methods

Year 2021, Volume: 16 Issue: 1, 163 - 177, 15.03.2021

Serkan Metin , Sengul Dogan

Abstract

Local descriptors are the most effective textural image recognition methods. Local descriptors generally consist of two phases. These are binary feature coding and histogram extraction phases, and they often use the signum function for the binary feature extraction. In this article, new fuzzy-based mathematical kernels are proposed for binary feature encoding in local descriptors. Fuzzy kernels consist of membership degree calculation and coding these membership degrees. In order to calculate membership degrees, four fuzzy sets are utilized. The proposed fuzzy kernels are considered as binary feature-extraction functions, and a novel textural image recognition architecture is created using these fuzzy kernels. These architecture phases are; (1) binary feature coding with fuzzy kernels, (2) calculating lower and upper images, (3) histogram extraction, (4) feature reduction with maximum pooling, (5) classification. In the classification phase, a quadratic kernel-based support vector machine (SVM) classifier is utilized. The presented fuzzy kernels are implemented on the Local Binary Pattern (LBP) and Local Graph Structure (LGS). 16 novel methods are presented using fuzzy kernels for each descriptor. In this article, LBP and LGS are used, and 32 novel fuzzy-based methods are proposed to improve recognition capability. 3 facial images and 3 textural image datasets are used to evaluate the methods' performance. The experimental results clearly illustrate that the fuzzy kernels based LBP and LGS methods have high facial and textural image recognition capability.

Keywords

Fuzzy coding, local binary pattern, local graph structure, texture recoginiton, biometrics

References

• [1] Li J, Sang N, Gao C. LEDTD: Local edge direction and texture descriptor for face recognition. Signal Processing: Image Communication. 2016;41:40-5.
• [2] Nguyen K, Fookes C, Jillela R, Sridharan S, Ross A. Long range iris recognition: A survey. Pattern Recognition. 2017;72:123-43.
• [3] Lekdioui K, Messoussi R, Ruichek Y, Chaabi Y, Touahni R. Facial decomposition for expression recognition using texture/shape descriptors and SVM classifier. Signal Processing: Image Communication. 2017;58:300-12.
• [4] Qin C, Chen X, Luo X, Zhang X, Sun X. Perceptual image hashing via dual-cross pattern encoding and salient structure detection. Information Sciences. 2018;423:284-302.
• [5] Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on pattern analysis and machine intelligence. 2002;24:971-87.
• [6] Ojala T, Pietikäinen M, Harwood D. A comparative study of texture measures with classification based on featured distributions. Pattern recognition. 1996;29:51-9.
• [7] Ojala T, Valkealahti K, Oja E, Pietikäinen M. Texture discrimination with multidimensional distributions of signed gray-level differences. Pattern Recognition. 2001;34:727-39.
• [8] Zhou H, Wang R, Wang C. A novel extended local-binary-pattern operator for texture analysis. Information Sciences. 2008;178:4314-25.
• [9] Fathi A, Naghsh-Nilchi AR. Noise tolerant local binary pattern operator for efficient texture analysis. Pattern Recognition Letters. 2012;33:1093-100.
• [10] Pei H, Yanqing S, Chaowei T, Siman Z. Center-symmetric local binary pattern based on weighted neighbor contribution. Optik. 2016;127:11599-606.
• [11] Chakraborty S, Singh SK, Chakraborty P. Local quadruple pattern: A novel descriptor for facial image recognition and retrieval. Computers & Electrical Engineering. 2017;62:92-104.
• [12] Bashier HK, Hoe LS, Hui LT, Azli MF, Han PY, Kwee WK, et al. Texture classification via extended local graph structure. Optik. 2016;127:638-43.
• [13] Abdullah MFA, Sayeed MS, Muthu KS, Bashier HK, Azman A, Ibrahim SZ. Face recognition with symmetric local graph structure (slgs). Expert Systems with Applications. 2014;41:6131-7.
• [14] Rakshit RD, Nath SC, Kisku DR. Face identification using some novel local descriptors under the influence of facial complexities. Expert Systems with Applications. 2018;92:82-94.
• [15] Abusham EE, Bashir HK. Face recognition using local graph structure (LGS). International Conference on Human-Computer Interaction: Springer; 2011. p. 169-75.
• [16] Ahonen T, Hadid A, Pietikäinen M. Face recognition with local binary patterns. European conference on computer vision: Springer; 2004. p. 469-81.
• [17] Tuncer T, Dogan S. Pyramid and multi kernel based local binary pattern for texture recognition. Journal of Ambient Intelligence and Humanized Computing. 2020;11:1241-52.
• [18] Tuncer T, Dogan S, Ertam F, Subasi A. A novel ensemble local graph structure based feature extraction network for EEG signal analysis. Biomedical Signal Processing and Control. 2020;61:102006.
• [19] Dong S, Yang J, Chen Y, Wang C, Zhang X, Park DS. Finger Vein Recognition Based on Multi-Orientation Weighted Symmetric Local Graph Structure. Ksii Transactions on Internet & Information Systems. 2015;9.
• [20] Vipparthi SK, Nagar SK. Local extreme complete trio pattern for multimedia image retrieval system. International Journal of Automation and Computing. 2016;13:457-67.
• [21] Samaria FS, Harter AC. Parameterisation of a stochastic model for human face identification. Proceedings of 1994 IEEE workshop on applications of computer vision: IEEE; 1994. p. 138-42.
• [22] Libor Spacek's Facial Image Database, Face94 Database, http://cswww.essex.ac.uk/mv/allfaces/faces94.html (accessed June 1, 2018).
• [23] Martinez AM, Kak AC. Pca versus lda. IEEE transactions on pattern analysis and machine intelligence. 2001;23:228-33.
• [24] Ojala T, Maenpaa T, Pietikainen M, Viertola J, Kyllonen J, Huovinen S. Outex-new framework for empirical evaluation of texture analysis algorithms. Object recognition supported by user interaction for service robots: IEEE; 2002. p. 701-6.
• [25] Tuncer T, Dogan S, Pławiak P, Acharya UR. Automated arrhythmia detection using novel hexadecimal local pattern and multilevel wavelet transform with ECG signals. Knowledge-Based Systems. 2019;186:104923.
• [26] Vapnik V. The support vector method of function estimation. Nonlinear Modeling: Springer; 1998. p. 55-85.