HMM-Based Face Recognition Using SVD and Half of the Face Image

Document Type : Research Paper

Authors

Electrical and Computer Engineering Faculty, Semnan University, Semnan, Iran.

Abstract

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.

Keywords

Main Subjects


[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.