Improving Facial Image Recognition based Neutrosophy and DWT Using Fully Center Symmetric Dual Cross Pattern

Full Text (PDF, 1039KB), PP.35-44

Views: 0 Downloads: 0


Turker Tuncer 1,* Sengul Dogan 1

1. Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey

* Corresponding author.


Received: 25 Feb. 2019 / Revised: 14 Mar. 2019 / Accepted: 21 Mar. 2019 / Published: 8 Jun. 2019

Index Terms

Fully Center Symmetric Dual Cross Pattern, Neutrosophy, DWT, Facial Image Recognition


Face recognition is one of the most commonly used biometric features in the identification of people. In this article, a novel facial image recognition architecture is proposed with a novel image descriptor which is called as fully center symmetric dual cross pattern (FCSDCP) The proposed architecture consists of preprocessing, feature extraction and classification phases. In the preprocessing phase, discrete wavelet transform (DWT) and Neutrosophy are used together to calculate coefficients of the face images. The proposed FCSDCP extracts features. LDA, QDA, SVM and KNN are utilized as classifiers. 4 datasets were chosen to obtain experiments and the results of the proposed method were compared to other state of art image descriptor based methods and the results clearly shows that the proposed method is a successful method for face classification.

Cite This Paper

Turker Tuncer, Sengul Dogan, "Improving Facial Image Recognition based Neutrosophy and DWT Using Fully Center Symmetric Dual Cross Pattern", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.11, No.6, pp. 35-44, 2019. DOI: 10.5815/ijigsp.2019.06.05


[1]Chang, T., Kuo, C. C.: ‘Texture analysis and classification with tree-structured wavelet transform’, IEEE T. Image Process., 1993, 2, (4), pp. 429-441.

[2]Gomez-Barrero, M, Rathgeb, C, Li, G, et al.: ‘Multi-biometric template protection based on bloom filters’, Inform. Fusion, 2018, 42, pp. 37-50.

[3]Zhang, Y., Shang, K., Wang, J., et al.: ‘Patch strategy for deep face recognition’, IET Image Process., 2018, 12, (5), pp. 819 – 825.

[4]Tome, P., Vera-Rodriguez, R., Fierrez, J.: ‘Facial soft biometric features for forensic face recognition’, Forensic Sci. Int., 2015, 257, pp. 271-284.

[5]Kwak, K. C., Pedrycz, W.: ‘Face recognition using a fuzzy fisherface classifier’, Pattern Recogn., 2015, 38 (10), pp. 1717-1732.

[6]Wen, G., Chen, H., Cai, D., et al.: ‘Improving face recognition with domain adaptation’, Neurocomputing, 2018, 287, pp. 45-51.

[7]Vo, D. M. Lee, S. W.: ‘Robust face recognition via hierarchical collaborative representation’, Inform. Sciences, 2018, 432, pp.332-346.

[8]Edmunds, T., Caplier, A.: ‘Face spoofing detection based on colour distortions’, IET Biometrics, 2017, 7, (1), pp. 27-38. 

[9]Pillai, A., Soundrapandiyan, R., Satapathy, S., et al.: ‘Local diagonal extrema number pattern: A new feature descriptor for face recognition’, Future Gener. Comp. Sy., 2018, 81, pp. 297-306.

[10]Yang, B., Chen, S.: ‘A comparative study on local binary pattern (LBP) based face recognition: LBP histogram versus LBP image’, Neurocomputing, 2013, 120, pp. 365-379.

[11]Chakraborty, S., Singh, S., Chakraborty, P.: ‘Local gradient hexa pattern: A descriptor for face recognition and retrieval’, IEEE T. Circ. Syst. Vid., 2016, 28, (1), pp. 171-180.

[12]Liu, L., Fieguth, P., Zhao, G., et al.: ‘Extended local binary patterns for face recognition’, Inform. Sciences, 2016, 358, pp. 56-72.

[13]Guo, Y., Cheng, D. H.: ‘New neutrosophic approach to image segmentation’, Pattern Recogn., 2009, 42, (5), pp. 587-595.

[14]Zhang, M., Zhang, L., Cheng, H. D.: ‘A neutrosophic approach to image segmentation based on watershed method’, Signal Process., 2010, 90, (5), pp. 1510-1517.

[15]Sengur, A., & Guo, Y.: Color texture image segmentation based on neutrosophic set and wavelet transformation. Computer Vision and Image Understanding, 2011, 115, (8), pp. 1134-1144.

[16]Tan, K. S., Lim, W. H., Isa, N. A. M., ‘Novel initialization scheme for Fuzzy C-Means algorithm on color image segmentation’, Appl. Soft Comput., 2013, 13, (4), pp.1832-1852.

[17]Koundal, D., Gupta, S., Singh, S.: ‘Computer aided thyroid nodule detection system using medical ultrasound images’, Biomed. Signal Proces., 2018, 40, pp. 117-130.

[18]Thanki, R. Borra, S.: ‘A color image steganography in hybrid FRT–DWT domain’, Journal of Information Security and Applications, 2018, 40, pp. 92-102.

[19]Sengur, A.: Wavelet transform and adaptive neuro-fuzzy inference system for color texture classification. Expert Systems with Applications, 2008, 34, (3), 2120-2128.

[20]Sangeetha, N., Anita, X., ‘Entropy based texture watermarking using discrete wavelet transform’, Optik, 2018, 160, pp. 380-388.

[21]Ojala, T., Pietikäinen, M., Mäenpää, T.: ‘Multiresolution gray-scale and rotation invariant texture classification with local binary patterns’, IEEE Trans. Pattern Anal. Mach. Intell., 2002, 24, (7), pp. 971–987.

[22]Pan, Z., Li, Z., Fan, H., et al.: ‘Feature based local binary pattern for rotation invariant texture classification’, Expert Syst. Appl., 2017, 88, pp. 238-248.

[23]Ding, C., Choi, J., Tao, D., et al.: ‘Multi-directional multi-level dual-cross patterns for robust face recognition’, IEEE Trans. Pattern Anal. Mach. Intell., 2016, 38, (3), pp. 518-531.

[24]Gao, Y., Qi, Y.: ‘Robust visual similarity retrieval in single model face databases’, Pattern Recogn., 2005, 38, (7), pp.1009-1020.

[25]Chakraborty, S., Singh, S. K., Chakraborty, P.: ‘Local quadruple pattern: A novel descriptor for facial image recognition and retrieval’, Comput. Electr. Eng.,2017, 62, pp. 92-104.

[26]Chakraborty, S., Singh, S. K., Chakraborty, P.: ‘Centre symmetric quadruple pattern: A novel descriptor for facial image recognition and retrieval’, Pattern Recogn. Lett., 2017, pp. 1-9.

[27]Abuzneid, M. A., Mahmood, A.: ‘Enhanced Human Face Recognition Using LBPH Descriptor, Multi-KNN, and Back-Propagation Neural Network’, IEEE Access, 2018, 6, pp. 20641-20651.

[28]Guo, G., Li, S. Z., Chan, K.: ‘Face recognition by support vector machines’, In Automatic Face and Gesture Recognition, 2000. Proceedings. Fourth IEEE International Conference on, 2000, pp. 196-201.

[29]Chen, L. F., Liao, H. Y. M., Ko, M. T., et al.: ‘A new LDA-based face recognition system which can solve the small sample size problem’, Pattern Recogn, 2000, 33, (10), pp. 1713-1726.

[30]Lu, J., Plataniotis, K. N., Venetsanopoulos, A. N.: ‘Regularized discriminant analysis for the small sample size problem in face recognition’, Pattern Recogn Lett, 2003, 24, (16), pp. 3079-3087.

[31]Kabacinski, R., Kowalski, M.: ‘Vein pattern database and benchmark results’, Electron Lett, 2011, 47, (20), pp. 1127 - 1128.

[32]Martinez A.M., Benavente, R.: ‘The AR Face Database’, CVC Technical Report,24, June 1998.

[33]Martínez , A.M., Kak , A.C., ‘PCA versus LDA’, IEEE Trans. Pattern Anal. Mach. Intell., 2001, 23, (2), pp. 228–233.

[34]Libor Spacek's Facial Image Database, “Face94Database”: (accessed Jan 1, 2018).

[35]Samaria F., Harter A.: ‘Parameterisation of a stochastic model for human face identification’, In Applications of Computer Vision, Proceedings of the Second IEEE Workshop on, 1994, pp. 138-142.

[36]Zhu, J., Wu, S., Wang, X., et al.: ‘Multi-image matching for object recognition’. IET Comput. Vis., 2018, 12, (3), pp. 350-356. 

[37]Kas, M., Ruichek, Y., and Messoussi, R., 'Mixed Neighborhood Topology Cross Decoded Patterns for Image-Based Face Recognition', Expert Systems with Applications, 2018, 114, pp. 119-142.

[38]Song, K., Yan, Y., Zhao, Y., and Liu, C., 'Adjacent Evaluation of Local Binary Pattern for Texture Classification', Journal of Visual Communication and Image Representation, 2015, 33, pp. 323-339.

[39]Yang, W., Wang, Z., and Zhang, B., 'Face Recognition Using Adaptive Local Ternary Patterns Method', Neurocomputing, 2016, 213, pp. 183-190.

[40]Fernández, A., Álvarez, M.X., and Bianconi, F., 'Image Classification with Binary Gradient Contours', Optics and Lasers in Engineering, 2011, 49, (9-10), pp. 1177-1184.

[41]Nanni, L., Brahnam, S., and Lumini, A., 'A Local Approach Based on a Local Binary Patterns Variant Texture Descriptor for Classifying Pain States', Expert Systems with Applications, 2010, 37, (12), pp. 7888-7894.

[42]Ruichek, Y., 'Local Concave-and-Convex Micro-Structure Patterns for Texture Classification', Pattern Recognition, 2018, 76, pp. 303-322.

[43]Rajput, S. and Bharti, D.J., 'A Face Recognition Using Linear-Diagonal Binary Graph Pattern Feature Extraction Method', International Journal in Foundations of Computer Science andTechnology (IJFCST, 2016, 6, (2), pp. 55-65.

[44]Rivera, A.R., Castillo, J.R., and Chae, O.O., 'Local Directional Number Pattern for Face Analysis: Face and Expression Recognition', IEEE transactions on image processing, 2013, 22, (5), pp. 1740-1752.

[45]Vipparthi, S.K. and Nagar, S.K., 'Local Extreme Complete Trio Pattern for Multimedia Image Retrieval System', International Journal of Automation and Computing, 2016, 13, (5), pp. 457-467.

[46]Silva, C., Bouwmans, T., and Frélicot, C., 'An Extended Center-Symmetric Local Binary Pattern for Background Modeling and Subtraction in Videos', in, International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISAPP 2015, (2015).

[47]Zeng, H., Chen, J., Cui, X., Cai, C., and Ma, K.-K., 'Quad Binary Pattern and Its Application in Mean-Shift Tracking', Neurocomputing, 2016, 217, pp. 3-10.