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HMM-Based Face Recognition Using SVD and Half of the Face Image | ||
| Journal of Modeling and Simulation in Electrical and Electronics Engineering | ||
| مقاله 5، دوره 1، شماره 2 - شماره پیاپی 4، آبان 2021، صفحه 45-50 اصل مقاله (844.88 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/mseee.2021.23031.1054 | ||
| نویسندگان | ||
| Kourosh Kiani* ؛ Sepideh Rezaeirad؛ Razieh Rastgoo | ||
| Electrical and Computer Engineering Faculty, Semnan University, Semnan, Iran. | ||
| تاریخ دریافت: 12 فروردین 1400، تاریخ بازنگری: 14 خرداد 1400، تاریخ پذیرش: 21 شهریور 1400 | ||
| چکیده | ||
| Speeding up the system is one of the basic challenges in the real-world applications of Face Recognition (FR), whereas reducing the computational complexity can significantly increase the speed of the system. In recent years, many face recognition methods have been proposed but few of them give attention to this issue. Accordingly, in this article, we take the axis-symmetrical property of faces as a novel idea to speed up the face recognition algorithm as well as to reduce the computational complexity. Taking the axis-symmetrical property of faces leads us to use half of the face image. Proposing a face recognition system using Hidden Markov Model (HMM) as a classifier, we use the Singular Value Decomposition (SVD) to build the observation vectors. Evaluated results of the proposed system on Yale and Faces94 datasets show that the proposed system can achieve a satisfactory recognition rate with a higher speed. | ||
| کلیدواژهها | ||
| Face Recognition؛ Hidden Markov Model (HMM)؛ Singular Value Decomposition (SVD)؛ Half of the face؛ Axis-symmetrical | ||
| مراجع | ||
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[1] Nezam Majidi, Kourosh Kiani, and Razieh Rastgoo. A deep model for super-resolution enhancement from a single image. Journal of AI and Data Mining, 8:451–460, 2020. [2] Alperen Kantarc and Hazım Kemal Ekenel, Thermal to Visible Face Recognition Using Deep Autoencoders, International Conference of the Biometrics Special Interest Group (BIOSIG), pp. 1-5, 2019. [3] Yifan Sun, Changmao Cheng1, Yuhan Zhang, Chi Zhang, Liang Zheng, Zhongdao Wang, Yichen Wei, Circle Loss: A Unified Perspective of Pair Similarity Optimization, arXiv:2002.10857v2, 2020. [4] Grigorios G. Chrysos, Stylianos Moschoglou, Giorgos Bouritsas, Jiankang Deng, Yannis Panagakis, Stefanos Zafeiriou, Deep Polynomial Neural Networks, arXiv:2006.13026v2, 2021. [5] Luiz A. Zanlorensi, Rayson Laroca, Diego R. Lucio, Lucas R. Santos, Alceu S. Britto Jr., and David Menotti, UFPR- Periocular: A Periocular Dataset Collected by Mobile Devices in Unconstrained Scenarios, arXiv:2011.12427v1, 2020. [6] X. Lv, M. Su, Z. Wang, Method Under Deep Learning Algorithm in Embedded Systems, Microprocessors and Microsystems, 2021. [7] Razieh Rastgoo, Kourosh Kiani, and Sergio Escalera. Hand sign language recognition using multi-view hand skeleton. Expert Systems With Applications, 150, 2020. [8] Razieh Rastgoo, Kourosh Kiani, and Sergio Escalera. Hand pose aware multimodal isolated sign language recognition. Multimedia Tools And Applications, 80:127–163, 2021. [9] Razieh Rastgoo, Kourosh Kiani, and Sergio Escalera. Real- time isolated hand sign language recognition using deep networks and SVD. Journal of Ambient Intelligence and Humanized Computing, 2021. [10] Razieh Rastgoo, Kourosh Kiani, and Sergio Escalera. Sign language recognition: A deep survey. Expert Systems With Application, 164:113794, 2021. [11] T. Pi , L. Zhang , B. Wang ,F. Li , Z. Zhang, Decision pyramid classifier for face recognition under complex variations using single sample per person, Pattern Recognition 64 (2017) 305–313. [12] M. Turk, A. Pentland, Eigenfaces for recognition, J. Cog. Neurosic. 3 (1) (1991)71–86. [13] M.A. Turk, A.P. Pentland, Face recognition using eigenfaces, in: Proceedings ofthe IEEE Conference on Computer Vision and Pattern Recognition 3–6 June,Maui, Hawaii, USA, 1991, pp. 586–591. [14] K. Etemad, R. Chellappa, Discriminant analysis for recognition of human faceimages, J. Opt. Soc. Am. A 14 (No. 8) (1997) 1724–1733. [15] P.N. Belhumeur, J.P. Hespanha, D.J. Kriegman, Eigenfaces vs. fisherfaces,recognition using class specific linear projection, in: Proc. 4th EuropeanConference on Computer Vision, 15–18 April, Cambridge, UK, 1996, pp. 45–58. [16] F. Chelali, A. Djeradi, and R. Djeradi, “Linear Discriminant Analysis for Face Recognition”, In Proc. of the International Conference on Multimedia Computing and Systems (MMCS. 2009), PP. 1-10, IEEE, 2009. [17] S. Nazeer, N. Omar, and M. Khalid, "Face Recognition System using Artificial Neural Networks Approach," in International Conference on Signal Processing, Communications and Networking (ICSCN '07), Feb. 2007, Chennai, India, PP. 420-425, IEEE, 2007. [18] G. Guo, S. Li, and K. Chan, "Face recognition by support vector machines", In Proc. of IEEE International Conference on Automatic Face and Gesture Recognition (FG „00), Grenoble, France, PP.196-201, IEEE, 2000. [19] Y. Chen, J. Su, Sparse embedded dictionary learning on face recognition, Pattern Recognition 64 (2017) 51–59. [20] X. Dong, X. Zhang, J. Sun, W. Wan, A two-stage learning approach to face recognition, J. Vis. Commun. Image R. 43 (2017) 21–29. [21] Y. Xu, Z. Zhang, G. Lu, J. Yang, Approximately symmetrical face images for image preprocessing in face recognition and sparse representation based classification, Pattern Recognition 54 (2016) 68–82. [22] P. Ekman, J.C. Hager, W.V. Friesen, The symmetry of emotional and deliberate facial actions, Psychophysiology 18 (2) (1981) 101–106. [23] F. Samaria and S. Young. HMM-based architecture for face identification. Image and Vision Computing, 12(1994)8, 537–543. [24] A. Nefian and M. Hayes. An embedded HMM-based approach for face detection and recognition. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, Phoenix, AZ, 1999, 3553–3556. [25] J. Lu, Y. Zhao, G. Lu, J. Yang, Dominant singular value decomposition representation for face recognition, Signal Processing 90 (2010) 2087–2093. [26] Cao, Danyang, and Bingru Yang. "An improved face recognition algorithm based on SVD." In Computer and Automation Engineering (ICCAE), 2010 the 2nd International Conference on, vol. 3, pp. 109-112. IEEE, 2010. [27] F. Samaria and F. Fallside. “Face identification and feature extraction using hidden markov models”, In G. Vernazza, editor, Image Processing: Theory and Applications. Elsevier, 1993. [28] A.V. Nefian, M.H. Hayes, Hidden markov models for face recognition,acoustics, speech and signal processing, Seattle, WA, in: Proceedings of the1998 IEEE International Conference on, vol. 5, 1998, pp. 2721–2724. [29] Xiang Ma, Dan Schonfeld, Ashfaq Khokhar, Image segmentation andclassification based on a 2D distributed hidden Markov model, Proc. SPIE6822, Visual Communications and Image Processing 2008, January 28 (2008)68221F. [30] Blunsom, Phil. "Hidden markov models." Lecture notes, August 15 (2004): 18-19. [31] Q. Miao, V. Makis, Condition monitoring and classification of rotating machine ryusing wavelets and hidden Markov models, Mech. Syst. Signal Process. 21(2007) 840–855. [32] L. Tao, C. Jin, D. Guangming, Zero crossing and coupled hidden Markov model for a rolling bearing performance degradation assessment, J. Vib. Control 20 (2014) 2487– 2500. [33] Z. Li, Y. He, F. Chu, J.Han, W. Hao, Fault recognition method for speed-up and speed-down process of rotating machinery based on independent component analysis and Factorial Hidden Markov Model, J. Sound Vib. 291 (2006) 60–71. [34] H. Jiang, J. Chen, G. Dong, T. Liu, G. Chen, Study on Hankel matrix-based SVD and its application in rolling element bearing fault diagnosis, Mech. Syst. Signal Process. 52 (2015) 338–359. [35] Rabiner, Lawrence R. "A tutorial on hidden Markov models and selected applications in speech recognition." Proceedings of the IEEE 77, no. 2 (1989): 257-286. [36] Q. Miao, V. Makis, Condition monitoring and classification of rotating machine ryusing wavelets and hidden Markov models, Mech. Syst. Signal Process. 21(2007) 840–855. [37] L.R. Rabiner, A tutorial on hidden Markov models and selected applications in speech recognition, Proc. IEEE 77 (1989) 257–286. [38] X. Yong, Z. Zhang, G. Lu, J. Yang, Approximately symmetrical face images for image preprocessing in face recognition and sparse representation based classification, Pattern Recognition 54 (2016): 68-82. [39] Tan, Xiaoyang, and Bill Triggs. "Enhanced local texture feature sets for face recognition under difficult lighting conditions." In International Workshop on Analysis and Modeling of Faces and Gestures, pp. 168-182. Springer Berlin Heidelberg, 2007. [40] Zhu, Jun-Yong, Wei-Shi Zheng, Feng Lu, and Jian-Huang Lai. "Illumination Invariant Single Face Image Recognition under Heterogeneous Lighting Condition." Pattern Recognition (2017). [41] Hu, Changhui, Xiaobo Lu, Mengjun Ye, and Weili Zeng. "Singular value decomposition and local near neighbors for face recognition under varying illumination." Pattern Recognition 64 (2017): 60-83. [42] Lee, Sanghun, and Chulhee Lee. "Multiscale morphology based illumination normalization with enhanced local textures for face recognition." Expert Systems with Applications 62 (2016): 347-357. [43] Davari, Pooya, and Hossein Miar Naimi. "A New Face Recognition System-Using HMMs along with SVD Coefficients." Visapp (2) (2008). [44] Lee, Jong Min, Seung-Jong Kim, Yoha Hwang, and Chang-Seop Song. "Diagnosis of mechanical fault signals using continuous hidden Markov model." Journal of Sound and Vibration 276, no. 3 (2004): 1065-1080. [45] Yang, Fanny, Sivaraman Balakrishnan, and Martin J. Wainwright. "Statistical and computational guarantees for the Baum-Welch algorithm." In Communication, Control, and Computing (Allerton), 2015 53rd Annual Allerton Conference on, pp. 658-665. IEEE, 2015. [46] Jiang, Huiming, Jin Chen, and Guangming Dong. "Hidden Markov model and nuisance attribute projection based bearing performance degradation assessment." Mechanical Systems and Signal Processing 72 (2016): 184-205. [47] Shen, Linlin, Zhen Ji, Li Bai, and Chen Xu. "DWT based HMM for face recognition." Journal of Electronics (China) 24, no. 6 (2007): 835-837. [48] Eickeler, Stefan, Stefan Müller, and Gerhard Rigoll. "Recognition of JPEG compressed face images based on statistical methods." Image and Vision Computing 18, no. 4 (2000): 279-287. [49] L.Spacek, Theessexfaces94database /http://cswww.essex. ac.uk/mv/all faces/S. [50] Chang, C.-C. and C.-J. Lin, LIBSVM: a library for support vector machines. ACM transactions on intelligent systems and technology (TIST), 2011. 2(3): p. 27. [51] Zhang, L., M. Yang, and X. Feng. Sparse representation or collaborative representation: Which helps face recognition? in Computer vision (ICCV), 2011 IEEE international conference on. 2011. IEEE. [52] Wright, J., et al., Robust face recognition via sparse representation. IEEE transactions on pattern analysis and machine intelligence, 2009. 31(2): p. 210-227. [53] Liu, B.-D., et al., Face recognition using class specific dictionary learning for sparse representation and collaborative representation. Neurocomputing, 2016. 204: p. 198-210. [54] Mandal, T., Q.J. Wu, and Y. Yuan, Curvelet based face recognition via dimension reduction. Signal Processing, 2009. 89(12): p. 2345-2353. [55] Mohammed, A.A., et al., Human face recognition based on multidimensional PCA and extreme learning machine. Pattern Recognition, 2011. 44(10): p. 2588-2597. | ||
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