Improving Face Recognition Rate Based on Histogram of Oriented Gradients and Difference of Gaussian

Subject Areas : Journal of Computer & Robotics

Sahar Iranpour Mobarakeh ¹ , Mehran Emadi ²

1 - Master Student, Faculty of Electrical Engineering, Mobarakeh Branch, Islamic Azad University, Mobarakeh ,Isfahan, Iran.
2 - Department of Electrical Engineering, Mobarakeh Branch, Islamic Azad University

Received: 2019-12-28 Accepted : 2020-05-12 Published : 2020-06-01

Keywords: face recognition, Light challenge, Difference of Gaussian, Histogram of Oriented Gradients,

Abstract :

Face recognition is a widely used identification method in the machine learning field because face biometrics are distinctive enough for detection and have more accessibility compared to other biometrics. Despite their merits, face biometrics have various challenges. Mainly, these challenges are divided into local and global categories. Local challenges can be addressed using sustainable methods against change while global challenges such as illumination challenges require powerful pre-processing methods. Therefore, in this study, a sustainable method against light changes has been proposed. In this method, two stages of the Difference of Gaussian have been utilized for the illumination normalization. Then, the features of the normalized image are extracted using Histogram of Oriented Gradient (HOG) and the feature vectors are classified using 3 k-nearest neighbor classifiers and the support vector machine with linear kernel, and the support vector machine with Radial Basis Function (RBF) kernel. Testing the proposed method on Computer Vision and Biometric Laboratory (CVBL) data indicated that the recognition rate, at best for the illumination challenge in the whole face and a part of the face is 98.6 % and 97.9% respectively.

References:

]1]S. Farokhi, S. M. Shamsuddin, U. Sheikh, and J. Flusser, "Near infrared face recognition: A comparison of moment-based approaches," in The 8th International Conference on Robotic, Vision, Signal Processing & Power Applications, 2014, pp. 129-135. 
[2]S. Farokhi, S. M. Shamsuddin, U. U. Sheikh, J. Flusser, M. Khansari, and K. Jafari-Khouzani, "Near infrared face recognition by combining Zernike moments and undecimated discrete wavelet transform," Digital Signal Processing, vol. 31, pp. 13-27, 2014. 
[3]S. Farokhi, U. U. Sheikh, J. Flusser, and B. Yang, "Near infrared face recognition using Zernike moments and Hermite kernels," Information Sciences, vol. 316, pp. 234-245, 2015. 
[4]R. Singh, M. Vatsa, H. S. Bhatt, S. Bharadwaj, A. Noore, and S. S. Nooreyezdan, "Plastic surgery: A new dimension to face recognition," IEEE Transactions on Information Forensics and Security, vol. 5, pp. 441-448, 2010. 
[5]C. Ding and D. Tao, "A comprehensive survey on pose-invariant face recognition," ACM Transactions on intelligent systems and technology (TIST), vol. 7, 
p. 37, 2016. 
[6]R. Gross and V. Brajovic, "An image preprocessing algorithm for illumination invariant face recognition," in International Conference on Audio-and Video-Based Biometric Person Authentication, 2003, pp. 10-18. 
[7]S. Shan, W. Gao, B. Cao, and D. Zhao, "Illumination normalization for robust face recognition against varying lighting conditions," in 2003 IEEE International SOI Conference. Proceedings (Cat. No. 03CH37443), 2003, pp. 157-164. 
[8]P.-H. Lee, S.-W. Wu, and Y.-P. Hung, "Illumination compensation using oriented local histogram equalization and its application to face recognition," IEEE Transactions on Image processing, vol. 21, pp. 4280-4289, 2012. 
[9]M. Li, X. Yu, K. H. Ryu, S. Lee ,and N. Theera-Umpon, "Face recognition technology development with Gabor, PCA and SVM methodology under illumination normalization condition," Cluster Computing, pp. 1-10, 2017. 
[10]P.-C. Chang, Y.-S. Chen, C.-H. Lee, C.-C. Lien, and C.-C. Han, "Illumination Robust Face Recognition Using Spatial Expansion Local Histogram Equalization and Locally Linear Regression Classification," in 2018 3rd International Conference on Computer and Communication Systems (ICCCS), 2018, pp. 249-253. 
[11]S. M. Pizer, R .E. Johnston, J. P. Ericksen, B. C. Yankaskas, and K. E. Muller, "Contrast-limited adaptive histogram equalization: speed and effectiveness," in [1990] Proceedings of the First authentication," in International Conference on Audio-and Video-Based Biometric Person Authentication, 2003, pp. 549-556. 
[17]Y. Cheng, Y. Hou, C. Zhao, Z. Li, Y. Hu, and C. Wang, "Robust face recognition based on illumination invariant in nonsubsampled contourlet transform domain," Neurocomputing, vol. 73, pp. 2217-2224, 2010. 
[18]S .Du and R. Ward, "Wavelet-based illumination normalization for face recognition," in IEEE International Conference on Image Processing 2005, 2005, pp. II-954. 
[19] Tabassum F, Islam MI, Khan RT, Amin MR. Human face recognition with combination of DWT and machine learning. Journal of King Saud University-Computer and Information Sciences. 2020 Feb 21.