انتخاب زیرمجموعه بهینه از ویژگیهای استخراجشده توسط عملگر بهینهشده LBP بر مبنای CLA - EC در سیستم بازشناسی چهره
محورهای موضوعی : مهندسی برق و کامپیوتراختر حضرتی بیشک 1 * , کریم فائز 2 , حسین برقی جند 3 , سجاد قطعی 4
1 - دانشگاه آزاد اسلامی واحد اهر
2 - دانشگاه صنعتی امیرکبیر
3 - دانشگاه آزاد اسلامی واحد اهر
4 - دانشگاه پیام نور تبریز
کلید واژه: آتاماتای یادگیر سلولی الگوی باینری محلی ماشین بردار پشتیبان محاسبات تکاملی,
چکیده مقاله :
ما در اين مقاله روش کارامد جديدی را مبتنی بر توصيفگر الگوی باينری محلی برای بازشناسی چهره معرفی کرديم. چون محاسبات داخل الگوی باینری محلی بین مقادیر دو پیکسل انجام میشود، حتی تغییرات کوچک در الگوی باینری عملکرد آن را تحت تأثیر قرار میدهد. در این مقاله یک روش جدید بازشناسی چهره برای انتخاب الگوهای باینری میانگین محلی (LABP) بر مبنای آتاماتای یادگیر سلولی مبتنی بر محاسبات تکاملی ارائه شده است. در روش پیشنهادی، ابتدا الگوهای باینری یکنواخت محلی توسط LABP از تصاویر چهره استخراج میشود. در LABPجهت به دست آوردن نمایش ویژگی مقاومتر، نقاط نمونه زیادی مورد استفاده قرار گرفته است، سپس بهترین زیرمجموعه از این الگوها بدون داشتن اطلاعات اولیه از آنها توسط روش CLA-ECپیدا شده و از آنها هیستوگرام گرفته میشود و در نهایت از ماشین بردار پشتیبان برای طبقهبندی استفاده میشود. نتیجه به دست آمده از شبیهسازی سیستمهای بازشناسی چهره روی مجموعه داده FERET، برتری الگوریتم پیشنهادی را نسبت به الگوریتمهای دیگر نشان داد.
In this paper, we present a new efficient method based on local binary pattern descriptor, for face recognition. Because, the calculations in Local binary pattern are done between two pixels values, so, small changes in the binary pattern affect its performance. In this paper, a new local average binary pattern descriptor is presented based on cellular learning automata and evolutionary computation (CLA-EC). In the proposed method, first, the LABP operator are used to extract uniform local binary patterns from face images; it should be noted that, in LABP operator to obtain more robust feature representation, many sample points has been used. Then, the best subset of patterns found by CLA-EC methods, and the histogram of these patterns is obtained. Finally, support vector machine is used for classification. The results of experiment on FERET data base show the advantage of the proposed algorithm compared to other algorithms.
[1] W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, "Face recognition: a literature survey," ACM Computing Survey, vol. 35, no. 4, pp. 399-458, Dec. 2003.
[2] M. Turk and A. Pentland, "Face recognition using eigenfaces," in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, CVPR'91, pp. 586-591, 3-6 Jun. 1991.
[3] P. Belhumeur, J. Hespanha, and D. Kriegman, "Eigenfaces vs fisherfaces: recognition using class specific linear projection," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711-720, Jul. 1997.
[4] D. Zhang, A. Kong, J. You, and M. Wong, "Online palmprint identification," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 25, no. 9, pp. 1041-1050, Sep. 2003.
[5] R. Gross, Handbook of Face Recognition, Springer-Verlag, 2005.
[6] R. Chellappa, C. L. Wilson, and S. Sirohey, "Human and machine recognition of faces: a survey," Proceedings of the IEEE, vol. 83, pp. 705-741, May 1995.
[7] B. Scholkopf, A. J. Smola, and K. R. Muller, "Nonlinear component analysis as a kernel eigenvalue problem," Neural Computation, vol. 10, no. 5, pp. 1299-1319, Jul. 1998.
[8] T. Ojala, M. Pietikainen, and M. Harwood, "A comparative study of texture measures with classification based on feature distributions," Pattern Recognition, vol. 29, no. 1, pp. 51-59, Jan. 1996.
[9] T. Ahonen, M. Pietikainen, A. Hadid, and T. Maneppa, "Face recognition based on the appearance of local regions," in Proc. Int. Conf. on Pattern Recognition, vol. 3, pp. 153-156, 23-26 Aug.. 2004.
[10] Z. Xie and G. Liu, "Infrared face recognition based on intensity of local micropattern-weighted local binary pattern," Optical Engineering, vol. 50, no. 7, Jul. 2011, doi: 10.1117/1.3594788.
[11] Z. Guo, L. Zhang, D. Zhang, and X. Mou, cHierarchical multiscale LBP for face and palm print recognition," in Proc. Int. Conf. on Image Processing, pp. 4521-4524, 26-29 Sep. 2010.
[12] J. Shelton, et al., "Genetic based LBP feature extraction and selection for facial recognition," in Proc. of 49th Annual Southeast Regional Conf., pp. 197-200, Kennesaw, GA,US, Mar. 2011.
[13] R. Mehta, J. Yuan, and K. Egiazarian, "Local polynomial approximation-local binary pattern (LPA-LBP) based face classification," in SPIE Proceedings, Multimedia on Mobile Devices 2011; and Multimedia Content Access: Algorithms and Systems, vol 7881, 8 pp. 11 Feb. 2011.
[14] S. Liao, X. Zhu, Z. Lei, L. Zhang, and S. Li, "Learning multi-scale block local binary patterns for face recognition," in: Lee, S. W., Li, S., Advances in Biometrics. Springer Berlin/Heidelberg, vol. 4642 of Lecture Notes in Computer Science, pp. 828-837, 2007.
[15] T. Ojala, M. Pietikainen, and T. Maenpaa, "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971-987, Jul. 2002.
[16] Z. Zhang, M. J. Lyons, M. Schuster, and S. Akamatsu, "Comparison between geometry-based and gabor-wavelets-based facial expression recognition using multi-layer perceptron," in Proc. 3rd Conf, Automatic Face and Gesture Recognition, pp. 454-459, 14-16 Apr. 1998.
[17] http://www.ee.oulu.fi/mvg/page/lbp_bibliography.
[18] R. Jensen, Combining Rough and Fuzzy Sets for Feature Selection, Ph.D. Thesis, University of Edinburgh, 2005.
[19] M. Kudo and J. Sklansky, "Comparison of algorithms that select features for pattern classifiers," Pattern Recognition, vol. 33, no. 1, pp. 25-41, 2000.
[20] T. P. Riopka and P. Bock, "Intelligent recombination using individual learning in a collective learning genetic algorithm," in Proc. of the Genetic and Evolutionary Computation Conf., GECCO'00, pp. 104-111, Las Vegas, Nevada, US, 10-12 Jul. 2000.
[21] D. Whitley, V. S. Gordon, and K. Mathias, "Lamarckian evolution, the baldwin effect and function optimization," Parallel Problem Solving from Nature III, Springer-Verlag, 1994.
[22] B. A. Julstrom, "Comparing darwinian, baldwinian, and lamarckian search in genetic algorithm for 4-cycle problem," Technical Report, Department of Computer Science, St. Cloud State University, St. Cloud, US, 1999.
[23] B. Masoodi, M. R. Meybodi, and M. Hashemi, "Cooperative CLA-EC," in Proc. of 12th Annual CSI Computer Conf. of Iran, Shahid Beheshti University, Tehran, Iran, pp. 558-559, Feb. 2007.
[24] B. Masoodifar, M. R. Meybodi, and R. Rastegar, "Asynchronous CLA-EC," in Proc. of 11th Annual CSI Computer Conf. of Iran, Tehran, Iran, pp. 447-458, Jan. 2006.
[25] R. Rastegar and M. R. Meybodi, "A new evolutionary computing model based on cellular learning automata," in Proc. of IEEE Int. Conf. on Cybernetics and Intelligent Systems, vol. 1, pp. 433-438 Singapore, 1-3 Dec. 2004.
[26] V. N. Vapnik, Statistical Learning Theory, Wiley, New York, 1998.
[27] M. Pontil and A. Verri, "Support vector machines for 3-d object recognition," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 20, no. 6, pp. 637-646, Jun. 1998.
[28] P. J. Philips, H. Moon, S. A. Rizvi, and P. J. Rauss, "The FERET evaluation methodology for face recognition algorithms," IEEE Trans. Pattern Analysis and Machine Analysis, vol. 22, no. 10, pp. 1090-1104, Oct. 2000.
[29] P. J. Phillips, H. Wechsler, J. Huang, and P. Rauss, "The FERET database and evaluation procedure for face recognition algorithms," Image Vis. Comput. J., vol. 16, no. 5, pp. 295-306, 1998.
[30] A. R. Martinez and R. Benavente, The AR Face Database, Barcelona, Spain, 1998.