%0 Journal Article %1 noauthororeditor %A Noor, %A Zahir, %A Mohammed, %A Song, %D 2018 %J IJIRIS:: International Journal of Innovative Research in Information Security %K %N Issue VII %P 453-464 %R doi://10.26562/IJIRIS.2018.SPIS10080 %T RATIONAL STRUCTURAL-ZERNIKE MEASURE: AN IMAGE SIMILARITY MEASURE FOR FACE RECOGNITION %U http://www.ijiris.com/volumes/Vol5/iss7/01.SPIS10080.pdf %V Volume V %X Image similarity or image distortion assessment is the underlying technology in many computer vision applications, and is the root of many algorithms used in image processing. Many similarity measures have been proposed with the aim of achieving a high level of accuracy, and each of these measures has its strength as well as its weaknesses. In this paper, we present a highly efficient hybrid measure for image similarity that is based on structural and momental measures. We propose a similarity measure called the rational structural-Zernike measure (ZSM), to determine a reliable similarity between any two images including human faces images. This measure combines the best features of two structural measures, the well-known structural similarity index measure (SSIM) and the feature similarity index for image quality assessment (FSIM), with Zernike moments (ZMs), which have proven effective in the extraction of image features. Simulation results show that the proposed measure outperforms the SSIM, FSIM , ZMs and the state-of-art measure Feature-Based Structural Measure (FSM) through its ability to detect similarity even under distortion and to recognise the similarity between images of human faces under various conditions of facial expression and pose.

@article{noauthororeditor, abstract = {Image similarity or image distortion assessment is the underlying technology in many computer vision applications, and is the root of many algorithms used in image processing. Many similarity measures have been proposed with the aim of achieving a high level of accuracy, and each of these measures has its strength as well as its weaknesses. In this paper, we present a highly efficient hybrid measure for image similarity that is based on structural and momental measures. We propose a similarity measure called the rational structural-Zernike measure (ZSM), to determine a reliable similarity between any two images including human faces images. This measure combines the best features of two structural measures, the well-known structural similarity index measure (SSIM) and the feature similarity index for image quality assessment (FSIM), with Zernike moments (ZMs), which have proven effective in the extraction of image features. Simulation results show that the proposed measure outperforms the SSIM, FSIM , ZMs and the state-of-art measure Feature-Based Structural Measure (FSM) through its ability to detect similarity even under distortion and to recognise the similarity between images of human faces under various conditions of facial expression and pose.}, added-at = {2018-09-24T16:42:23.000+0200}, author = {Noor and Zahir and Mohammed and Song}, biburl = {https://www.bibsonomy.org/bibtex/2389050586753bf269610a2810f537e5b/ijiris}, doi = {doi://10.26562/IJIRIS.2018.SPIS10080}, interhash = {7dcfd3cddbe689b7c3daa33c07b12476}, intrahash = {389050586753bf269610a2810f537e5b}, issn = {2349-7017}, journal = {IJIRIS:: International Journal of Innovative Research in Information Security}, keywords = {}, language = {English}, month = {September 2018}, note = {1. Hassan, Asmhan F., Dong Cailin, and Zahir M. Hussain. "An information- theoretic image quality measure: Comparison with statistical similarity." (2014). 2. Hashim, A.N. and Z.M. Hussain. Novel imagedependent quality assessment measures. in J. Comput. 2014. Citeseer. 3. Wang, Z., et al., Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 2004. 13(4): p. 600-612. 4. Sampat, M.P., Z. Wang, S. Gupta, A.C. Bovik and M.K. Markey, 2009. Complex wavelet structural similarity: A new image similarity index. IEEE Trans. Image Proc., 18: 2385-2401. DOI:10.1109/TIP.2009.2025923 5. Dan, L., D.Y. Bi and Y. Wang, 2010. Image quality assessment based on DCT and structural similarity. Proceedings of the 6th International Conference on Wireless Communications Networking and Mobile Computing, Sept. 23-25, IEEE Xplore Press, Chengdu, pp: 1-4. DOI: 10.1109/WICOM.2010.5600663 6. Zhang, L., et al., FSIM: A feature similarity index for image quality assessment. IEEE transactions on Image Processing, 2011. 20(8): p. 2378-2386. 7. Zhao, W., et al., Face recognition: A literature survey. ACM computing surveys (CSUR), 2003. 35(4): p. 399-458. 8. Barrett, W.A. A survey of face recognition algorithms and testing results. in Signals, Systems & Computers, 1997. Conference Record of the Thirty-First Asilomar Conference on. 1997. IEEE. 9. Hu, Y. and Z. Wang. A similarity measure based on Hausdorff distance for human face recognition. in Pattern Recognition, 2006. ICPR 2006. 18th International Conference on. 2006. IEEE. 10. Hashim, A.N. and Z. Hussain, Local and semi-global feature-correlative techniques for face recognition. IJACSA, 2014. 11. Hassan, A.F., Z. Hussain, and D. Cai-lin, An Information-Theoretic Measure for Face Recognition: Comparison with Structural Similarity. IJARAI. 2014. 12. Shnain, N.A., Z.M. Hussain, and S.F. Lu, A Feature-Based Structural Measure: An Image Similarity Measure for Face Recognition. Applied Sciences, 2017. 7(8): p. 786. 13. Shnain, Noor Abdalrazak, Song Feng Lu, and Zahir M. Hussain. "HOS image similarity measure for human face recognition." Computer and Communications (ICCC), 2017 3rd IEEE International Conference on. IEEE, 2017. 14. Aljanabi, Mohammed Abdulameer, Noor Abdalrazak Shnain, and Song Feng Lu. "An image similarity measure based on joint histogram—Entropy for face recognition." Computer and Communications (ICCC), 2017 3rd IEEE International Conference on. IEEE, 2017. 15. Teh, C.-H. and R.T. Chin, On image analysis by the methods of moments. IEEE Transactions on pattern analysis and machine intelligence, 1988. 10(4): p. 496-513. 16. Lajevardi, S.M. and Z.M. Hussain, Higher order orthogonal moments for invariant facial expression recognition. Digital Signal Processing, 2010. 20(6): p. 1771-1779. 17. Farajzadeh, N., K. Faez, and G. Pan, Study on the performance of moments as invariant descriptors for practical face recognition systems. IET Computer Vision, 2010. 4(4): p. 272-285. 18. Ono, A., Face recognition with Zernike moments. Systems and Computers in Japan, 2003. 34(10): p. 26-35. 19. Singh, C., N. Mittal, and E. Walia, Face recognition using Zernike and complex Zernike moment features. Pattern Recognition and Image Analysis, 2011. 21(1): p. 71-81. 20. Shi, Z., G. Liu, and M. Du, Rotary face recognition based on pseudo Zernike moments. Emerging Comput. Inf. Technol. Educ. Adv. Intell. Soft Comput, 2012. 146: p. 641-646. 21. Wang, Z. and E.P. Simoncelli. Translation insensitive image similarity in complex wavelet domain. in Acoustics, Speech, and Signal Processing, 2005. Proceedings.(ICASSP'05). IEEE International Conference on. 2005. IEEE. 22. Hwang, S.-K. and W.-Y. Kim, A novel approach to the fast computation of Zernike moments. Pattern Recognition, 2006. 39(11): p. 2065-2076. 23. Canny, J., A computational approach to edge detection. IEEE Transactions on pattern analysis and machine intelligence, 1986(6): p. 679-698. 24. N. Ponomarenko, V. Lukin, A. Zelensky, K. Egiazarian, M. Carli, and F. Battisti, “TID2008—A database for evaluation of full-reference visual quality assessment metrics,” Adv. Modern Radioelectron., vol. 10, pp. 30–45, 2009. 25. AT&T Laboratories, The Database of Faces, Cambridge [online], ©2002 [accessed 10/09/2014]. Available from: http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html 26. Tom Fawcett, "An introduction to ROC analysis," Pattern Recognition Letters, 2006. 27. Seedahmed S. Mahmoud, Zahir M. Hussain, and Peter O’Shea, “A Geometrical-Based Microcell Mobile Radio Channel Model,” Wireless Networks, Springer, vol. 12, no. 5, pp. 653-664, 2006. 28. Seedahmed S. Mahmoud, Zahir M. Hussain, and Peter O’Shea, “Geometrical Model for Mobile Radio Channel with Hyperbolically Distributed Scatterers,” IEEE International Conference on Communication Systems (ICCS 2002), Singapore, Nov. 2002. 29. Yuu-Seng Lau and Zahir M. Hussain, “A New Approach in Chaos Shift Keying for Secure Communication,” Proceedings of the IEEE International conference on Information Theory and Its Applications (ICITA’2005), Sydney, Australia, 4-7 Jul. 2005.}, number = {Issue VII}, pages = {453-464}, timestamp = {2018-09-24T16:42:23.000+0200}, title = {RATIONAL STRUCTURAL-ZERNIKE MEASURE: AN IMAGE SIMILARITY MEASURE FOR FACE RECOGNITION}, url = {http://www.ijiris.com/volumes/Vol5/iss7/01.SPIS10080.pdf}, volume = {Volume V}, year = 2018 }