Optimization technique to linear discriminant regression for face recognition

  • Authors

    • T. Syed Akheel JNTU
    • Dr. V.Usha Shree JNTUH
    • Dr. S.Aruna Mastani JNTU
    2019-07-14
    https://doi.org/10.14419/ijet.v7i4.20260
  • Face Recognition, Active Appearance Model, Linear Collaborative Discriminant Regression Classification, Whale Optimization.

  • Abstract

    To improve the robustness of the linear regression model number of improvements have been made working on the different databases, the main aim of this paper is to show how an optimization algorithms improves the efficiency of linear discriminant regression methods and performance is evaluated, The features are extracted using Active Appearance Model then the classification is done via linear collaborative Discriminant regression classification (LCDRC) model Proposed by Xiaochao Qu. In the LCDRC classifier, the most important evaluation is projection matrix that might get multiplied to the features while classification. In order to select the optimal projection matrix, this paper proposes a improved whale optimization technique, which is the Enhanced form of Whale Optimization Algorithm (WOA). The proposed face recognition model compares its performance over other  conventional methods by varying the regularization constant value from0.5 to 2.5 and performance is taken in terms of measures like Accuracy, Specificity, Sensitivity, Precision, Negative Predictive Value (NPV), F1Score and Matthews Correlation Coefficient (MCC), False positive rate (FPR), False negative rate (FNR) and False Discovery Rate (FDR),and the efficiency by varying the regularization constant and the effectiveness of this model is proven.

     

     

  • References

    1. [1] Fuzzy Linear Regression Discriminant Projection for Face Recognition P Pu Hunag et al ,date of publication March 9, 2017,.Digital Object Identifier 10.1109/ACCESS.2017.

      [2] Jing-Wein Wang, Ngoc Tuyen Le, Jiann-ShuLee and Chou-Chen Wang, "Color face image enhancement using adaptive singular value decomposition in fourier domain for face recognition," Pattern Recognition, vol. 57, pp. 31-49, September 2016.https://doi.org/10.1016/j.patcog.2016.03.021.

      [3] Xiaochao Qu, Suah Kim, Run Cui, Hyoung Joong Kim, “Linear collaborative discriminant regression classification for face recognitionâ€, Journal of Visual Communication and Image Representation, Vol. 31, pp. 312–319, 2015.https://doi.org/10.1016/j.jvcir.2015.07.009.

      [4] Linear Discriminant Regression Classiï¬cation for Face Recognition Shih-Ming Huang and Jar-Ferr Yang, Fellow, IEEE Signal Processing Letters,Vol.20,No.1,January2013https://doi.org/10.1109/LSP.2012.2230257.

      [5] S.-M. Huang and J.-F. Yang, “Improved principal component regression for face recognition under illumination variations,†IEEE Signal Process. Lett., vol. 19, no. 4, pp. 179–182, Apr. 2012.. https://doi.org/10.1109/LSP.2012.2185492.

      [6] Face Recognition Using Nearest Feature Space Embedding, Ying-Nong Chen, Et.al, IEEE, Cheng-Tzu Wang, and Kuo-Chin Fan Face Recognition Using Nearest Feature Space Embedding," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 33, no. pp. 1073-1086, 2010.https://doi.org/10.1109/TPAMI.2010.197.

      [7] P. Belhumeur, J. Hespanha, and D. Kriegman, “Eigenfaces vs. ï¬sherfaces:Recognitionusingclassspeciï¬clinearprojection,â€IEEETrans. Patt. Anal. Mach. Intel., vol. 19, no. 7, 1997.https://doi.org/10.1109/34.598228.

      [8] Facial Feature Extraction Using an Active Appearance Model on the iPhone.2014 Eighth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing.

      [9] I. Naseem, R. Togneri, and M. Bennamoun, “Linear regression for face recognition,†Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 32, no. 11, pp. 2106–2112, 2010. https://doi.org/10.1109/TPAMI.2010.128.

      [10] Turk M Pentland A"Eigenfaces for recogntion" Journal of cognitive neurosci vol 3 no 171-86(1991)https://doi.org/10.1162/jocn.1991.3.1.71.

      [11] Belhumeur, J. Hespanha and D. Kriegman, “Eigenfaces vs. fisherfaces: Recognition using class specific linear projection,†IEEE Trans.Patt. Anal. Mach. Intel., vol. 19, no. 7, 1997, pp 711-720. https://doi.org/10.1109/34.598228.

      [12] Jiuzhen Liang, Mei Wang, Zhilei Chai and Qin Wu, "Different lighting processing and feature extraction methods for efficient face recognition," IET Image Process., vol. 8, no.. 9, pp. 528–538, 2014.https://doi.org/10.1049/iet-ipr.2013.0006.

      [13] Hayet Boughrara, Mohamed Chtourou, Chokri Ben Amar and Liming Chen, "Face recognition based on perceived facial images and multilayer perceptron neural network using constructive training algorithm," IET Comput. Vis., vol. 8, no. 6, pp. 729–739, 2014.https://doi.org/10.1049/iet-cvi.2013.0294.

      [14] W. Chen and Y. Gao, "Face Recognition Using Ensemble String Matching," IEEE Transactions on Image Processing, vol. 22, no. 12, pp. 4798-4808, Dec. 2013.https://doi.org/10.1109/TIP.2013.2277920.

      [15] S. Chakraborty, S. K. Singh and P. Chakraborty, "Local Gradient Hexa Pattern: A Descriptor for Face Recognition and Retrieval," IEEE Transactions on Circuits and Systems for Video Technology, vol. 28, no. 1, pp. 171-180, Jan. 2018.https://doi.org/10.1109/TCSVT.2016.2603535.

      [16] Seyedali Mirjalili, Seyed Mohammad Mirjalili and Andrew Lewis, " Grey Wolf Optimizer", Advances in Engineering Software, vol.69, pp.46–61, 2014.https://doi.org/10.1016/j.advengsoft.2013.12.007.

      [17] A.H. Gandomi , X.-S. Yang , S. Talatahari and A.H. Alavi, " Firefly algorithm with chaos", Commun Nonlinear Sci Numer Simulat, vol. 18, pp. 89–98, 2013.https://doi.org/10.1016/j.cnsns.2012.06.009.

      [18] M.E.H.Pedersen and A.J.Chipperfield, " Simplifying Particle Swarm Optimization", Applied Soft Computing, vol. 10, no. 2, pp. 618-628, 2010.https://doi.org/10.1016/j.asoc.2009.08.029.

      [19] D.Karaboga and B.Basturk, " On the performance of artificial bee colony (ABC) algorithm", Applied Soft Computing, vol. 8, no. 1, pp. 687-697, 2008.https://doi.org/10.1016/j.asoc.2007.05.007.

      [20] JohnMcCall, "Genetic algorithms for modelling and optimisation", Journal of Computational and Applied Mathematics, vol. 184, no. 1, pp. 205-222, 2005.https://doi.org/10.1016/j.cam.2004.07.034.

      [21] K. Padmavathi1and K. Thangadurai, " Implementation of RGB and Grayscale Images in Plant Leaves Disease Detection – Comparative Study", Indian Journal of Science and Technology, vol. 9, no. 6, 2016.https://doi.org/10.17485/ijst/2016/v9i6/77739.

      [22] Amit Sarkar and T. Senthil Murugan, " Cluster head selection for energy efficient and delay-less routing in wireless sensor network", Wireless Networks, pp. 1-18, 2017.https://doi.org/10.1007/s11276-017-1558-2.

      [23] R. Kaur and S. Kaur, "Comparison of contrast enhancement techniques for medical image," 2016 Conference on Emerging Devices and Smart Systems (ICEDSS), Namakkal, pp. 155-159,2016.https://doi.org/10.1109/ICEDSS.2016.7587782.

      [24] I.Naseem,R.Togneri,andM.Bennamoun,“Robustregressionforface recognition,â€Patt.Recognit.,vol.45,no,1,pp.104–118,Jan.2012.https://doi.org/10.1016/j.patcog.2011.07.003.

      [25] Xiaochao Qu,Hyoung Joong Kim "Ehanced discrimiantn linea regression classification for face recogntiion" 2014 IEEE international conference on intellegetn sensors ,sensorsnetwroks and information processinf (ISSN) Singapur(21-24)April 2014.

      [26] Amit Sarkar and T. Senthil Murugan, " Cluster head selection for energy efficient and delay-less routing in wireless sensor network", Wireless Networks, pp. 1-18, 2017.https://doi.org/10.1007/s11276-017-1558-2.

      [27] R. Kaur and S. Kaur, "Comparison of contrast enhancement techniques for medical image," 2016 Conference on Emerging Devices and Smart Systems (ICEDSS), Namakkal, pp. 155-159,2016.https://doi.org/10.1109/ICEDSS.2016.7587782.

      [28] Seyedali Mirjalili and Andrew Lewis, " The Whale Optimization Algorithm", Advances in Engineering Software, vol. 95, pp.51–67, 2016.https://doi.org/10.1016/j.advengsoft.2016.01.008.

      [29] Seyedali Mirjalili, Seyed Mohammad Mirjalili and Andrew Lewis, " Grey Wolf Optimizer", Advances in Engineering Software, vol.69, pp.46–61, 2014https://doi.org/10.1016/j.advengsoft.2013.12.007.

      [30] A.H. Gandomi , X.-S. Yang , S. Talatahari and A.H. Alavi, " Firefly algorithm with chaos", Commun Nonlinear Sci Numer Simulat, vol. 18, pp. 89–98, 2013.https://doi.org/10.1016/j.cnsns.2012.06.009.

      [31] M.E.H.Pedersen and A.J.Chipperfield, " Simplifying Particle Swarm Optimization", Applied Soft Computing, vol. 10, no. 2, pp. 618-628, 2010.https://doi.org/10.1016/j.asoc.2009.08.029.

      [32] D.Karaboga and B.Basturk, " On the performance of artificial bee colony (ABC) algorithm", Applied Soft Computing, vol. 8, no. 1, pp. 687-697, 2008.https://doi.org/10.1016/j.asoc.2007.05.007.

      [33] JohnMcCall, "Genetic algorithms for modelling and optimisation", Journal of Computational and Applied Mathematics, vol. 184, no. 1, pp. 205-222, 2005.https://doi.org/10.1016/j.cam.2004.07.034.

      [34] T F Cootes G J Edwards and C taylor active appearance model proc european conferece computer vision 1998 vol 2 pp 484-498 springer 1998.https://doi.org/10.1007/BFb0054760.

  • Downloads

  • How to Cite

    Syed Akheel, T., V.Usha Shree, D., & S.Aruna Mastani, D. (2019). Optimization technique to linear discriminant regression for face recognition. International Journal of Engineering & Technology, 7(4), 6741-6746. https://doi.org/10.14419/ijet.v7i4.20260

    Received date: 2018-09-25

    Accepted date: 2018-12-08

    Published date: 2019-07-14