Adaptive hybrid technique for face recognition

Hazeem B. Taher, Kadhim M. Hashim, Atheer Yousif Oudah


One of the most important biometric features for personal identification is the face. In current paper, a new method of face verification upon on singular value decomposition (SVD) and standard deviation (SD) would be described. Due to many variations in real-life such as pose, illumination, or facial expression, there would be difficulty of face recognition. It should be mentioned that there are many approaches for face recognition, however, there is no one could be considered as the most suitable for many situations. One of the methods used is Singular value vector for an image detecting, but the drawback of this approach is the low rate of recognition, where one scale singular value vector is used for face acknowledgment. There an algorithm has been developed to expand the rate of the recognition. In this paper, an approach has been proposed to associate two feature sets obtained from SVD and SD method. It has noticed a good recognition rate could be obtained from the experimental results, where approximately more that 97.5% recognition rate has obtained on the ORL data base. The results from current proposed method have matched with some techniques and it has shown that this method is better than the existing approaches. An extensive experiment has demonstrated not only better performance, but it offers a great likely to achieve equivalent performance to other categories of state-of-the-art methods.


Pattern recognition Face Recognition Singular Value Decomposition (SVD) Standard Deviation (SD)

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Riddhi Patel and Shruti B. Yagnik, “A Literature Survey on Face Recognition Techniques,” International Journal of Computer Trends and Technology (IJCTT), Vol. 5, No. 4, pp. 189-194, 2013.

W. Zhao, R. Chellappa, P. J. Phillips and A. Rosenfeld, “Face Recognition: A Literature Survey,” CM Computing Surveys, Vol. 35, No. 4, pp. 399-458, 2003.

Jiazhong He and Minghui Du, “Face Recognition Based on Projection Map and SVD Method for One Training Image per Person,” International Conference on Computational Intelligence for Modelling, Control and Automation, and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC’05), 2005.

M. Turk and A. Pentland, “Eigenfaces for Recognition,” Journal of Cognitive Neurosicence, Vol. 3, No. 1, pp. 71-86, 1991.

J. Lu, K. N. Plataniotis and A. N. Venetsanopoulos, “Face recognition using LDA-based algorithms,” IEEE Trans., Neural Networks, vol.14, pp.195-200, 2003.

Z. Hong, “Algebraic feature extraction of image for recognition,” Pattern Recognition,Vol.24, pp. 211-219, 1991.

Y Cheng, K.Liu.J.Jang, Y.Zhang and N.Gu, “Human face recognition method based on the statistical model of small sample size,” SPIE Proc: Intell. Robots and Compu. Vision,Vol.1607, pp. 85-95,1991.

Messaoud B., Lamia M., Farid H., Abderrazak G. and Mohamed C. “Score Fusion of SVD and DCT-RLDA for Face Recognition,” In Proceedings of IEEE International Conference on Image Processing theory, Tools and Applications, p. 1-8, 2008.

H. Miar-Naimi and P. Davari, “A New Fast and Efficient HMM-Based Face Recognition System Using a 7-State HMM Along With SVD Coefficients,” Iranian Journal of Electrical & Electronic Engineering, Vol. 4, Nos. 1 & 2, 2008.

Yanwei Pang, NenghaiYn, Kong Zhang, JiaweiRong and Zhengkai Liu, “Fusion Of SVD And LDA For Face Recognition,” International Conference on Image Processing (ICIP), pp. 1417-1420, 2004.

Jia-Zhong He , Ming-Hui Du, Sheng-Wei Pei and Quan Wan, “Face Recognition Based On Spectroface And Uniform Eigen-Space SVD For One Training Image Per Person,” Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, pp. 18-21, 2005.

M. S. Bartlell, J. R. Movellan and T. J. Se Jnow Ski, “Face Recognition by Independent Component Analysis,” IEEE Transon Neural Networks, Vol. 13, No. 6, pp. 1450- 1464, 2002.

Lindsay I Smith, A tutorial on Principal Components Analysis, February 26, 2002.

Allan G. Bluman, Elementary Statistics A Step by Step Approach, The McGraw-Hill Companies, Inc., 2009.

DK H PHM Yassin, S Hoque, and F. Deravi, “Age Sensitivity of Face Recognition Algorithms”, International Conference on Emerging Security Technologies, pp. 12-15, 2013.

ORL, 1992. The ORL face database at the AT&T (Olivetti) Research Laboratory. Available from: .

Zhi R. and Ruan Q., “Two-dimensional direct and weighted linear discriminant analysis for face recognition”,Neurocomputing, pp. 3607–3611, 2008.

Zhou D., Yang X., Peng N. and Wang Y,“Improved-LDA based face recognition using both facial global and local information”,Pattern Recognition Letters, pp. 536-543, 2006.

Jin Zhang, You Wang, Guang Li and Walter J. Freeman, “Application of Bionic Neural Network on Face Recognition Based on SVD and DCT”, World Congress on Intelligent Control and Automation, pp. 2733–2736, 2004.

Hazeem B. Taher, Kadhim M. Hashem, Fatima A. Sajet, " Proposed Method For Road Detection and Following Boundaries" ,, 2018,Vol.96, No. 18 p(6106-6116).

B. Durakovic and Basic, H., “Continuous Quality Improvement in Textile Processing by Statistical Process Control Tools: A Case Study of Medium-Sized Company”, Periodicals of Engineering and Natural Sciences, vol. 1, no. 1, pp. 39-47, 2013.

B. Durakovic, "Design of Experiments Application, Concepts, Examples: State of the Art," Periodicals of Engineering and Natural Scinces, vol. 5, no. 3, p. 421‒439, 2017.

B. Durakovic, “Emerging Issues, Trends and Challenges for Sustainable Engineering”, The Sixth Regional Conference on Soft Computing 2017. 2017.

M. Inalpolat and Durakovic, B., “Implementation of Advanced Automated Material Handling Systems in Manufacturing Environment”, European Conference of Technology and Society - EuroTecS. 2013.



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Copyright (c) 2019 Hazeem B. Taher, Kadhim M. Hashim, Atheer Yousif Oudah

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ISSN: 2303-4521

Digital Object Identifier DOI: 10.21533/pen

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License