Facial recognition technology is transformative in security and human-machine interaction, reshaping societal interactions. Robust descriptors, essential for high precision in machine learning tasks like recognition and recall, are integral to this transformation. This paper presents a hybrid model enhancing local binary pattern descriptors for facial representation. By integrating rotation-invariant local binary pattern with uniform rotation-invariant grey-level co-occurrence, employing linear discriminant analysis for feature space optimization, and utilizing an artificial neural network for classification, the model achieves exceptional accuracy rates of 100% for Olivetti Research Laboratory, 99.98% for Maastricht University Computer Vision Test, and 99.17% for Extended Yale B, surpassing traditional methods significantly.
B. Johnston and P. de Chazal, “A review of image-based automatic facial landmark identification techniques,” EURASIP J. Image Video Process., vol. 2018, 2018, Art no. 86.
M. A. Rahim, M. N. Hossain, T. Wahid, and M. S. Azam, “Face recognition using local binary patterns (LBP),” Glob. J. Comput. Sci. Technol. Graphics Vis., vol. 13, no. 4, pp. 1–8, 2013.
M. Rizon, M. F. Hashim, P. Saad, S. Yaacob, M. R. Mamat, A. Y. M. Shakaff, A. R. Saad, H. Desa, and M. Karthigayan, “Face recognition using eigenfaces and neural networks,” Am. J. Appl. Sci., vol. 3, no. 6, pp. 1872–1875, 2006.
P. S. Hiremath and C. J. Prabhakar, “Face recognition technique using symbolic PCA method,” in International Conference on Pattern Recognition and Machine Intelligence, Kolkata, India, December 20–22, 2005, pp. 266–271.
J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal Mach Intell., vol. 31, no. 2, pp. 210–227, 2008.
D. Zhang, Y. Xu, and W. Zuo, “Sparse representation-based methods for face recognition,” Discriminative Learning in Biometrics, Singapore: Springer, 2016, pp. 199–214.
Z. Yu, Y. Dong, J. Cheng, M. Sun, and F. Su, “Research on Face Recognition Classification Based on Improved GoogleNet,” Security Commun. Networks, vol. 2022, 2022, Art no. 7192306.
L. O. Omotosho, I. K. Ogundoyin, J. O. Oyeniyi, and O. A. Oyeniran, “A real time face recognition system using Alexnet deep convolutional network transfer learning model,” J. Eng. Stud. Res., vol. 27, no. 2, pp. 82–88, 2021.
M. A. A. Aziz, S. Ismail, and N. Allias, “Deep learning in face recognition for attendance system: an exploratory study,” J. Comput. Res. Innovation, vol. 7, no. 2, pp. 74–81, 2022.
H. Xu, “The application of deep learning-based face recognition system in public safety,” in Proc. of International Conference on Cloud Computing, Performance Computing, and Deep Learning, Wuhan, China, October 13, 2022, pp. 459–463.
Saurabh, Jindal, and Sonika, “Face recognition under variation in illumination using hybrid of PCA and DCP,” Int. J. Adv. Res. Comput. Sci., vol. 8, no. 8, pp. 727–731, 2017.
A. Tharwat, T. Gaber, A. Ibrahim, and A. E. Hassanien, “Linear discriminant analysis: A detailed tutorial,” AI Commun., vol. 30, no. 2, pp. 169–190, 2017.
I. Das, I. Gangopadhyay, and A. Chatterjee, “face detection and recognition using HAAR classifier and LBP histogram,” Int. J. Adv. Res. Comput. Sci., vol. 9, no. 2, pp. 592–598, 2018.
R. Sharma and R. Arora, “Face recognition using LTP algorithm,” Int. J. Sci. Res., vol. 4, no. 12, pp. 2140–2142, 2015.
B. Yang and S. Chen, “A comparative study on local binary pattern (LBP) based face recognition: LBP histogram versus LBP image,” Neurocomputing, vol. 120, pp. 365–379, 2013.
P. V Bankar and A. C. Pise, “Face recognition by using Gabor and LBP,” in Proc. of 2015 International Conference on Communications and Signal Processing, Melmaruvathur, India, April 2–4, 2015, pp. 45–48.
H. T. M. Nhat and V. T. Hoang, “Feature fusion by using LBP, HOG, GIST descriptors and canonical correlation analysis for face recognition,” in Proc. of 2019 26th International Conf. on Telecommunications, Hanoi, Vietnam, April 8–10, 2019, pp. 371–375.
D. G. Lowe, “Object recognition from local scale-invariant features,” in Proc. of the Seventh IEEE International Conf. on Computer Vision, Kerkyra, Greece, September 20–27, 1999, pp. 1150–1157.
H. Bay, T. Tuytelaars, and L. Van Gool, “Speeded-up robust features,” in 9th European Conference on Computer Vision, Pattern Recognition, and Graphics, Graz, Austria, May 7–13, 2006, Lecture Notes in Computer Science, vol. 3954, 2008, pp. 346–359.
E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, “ORB: An efficient alternative to SIFT or SURF,” in Proc. of 2011 International Conf. on Computer Vision, Barcelona, Spain, November 6–13, 2011, pp. 2564–2571.
S. Leutenegger, M. Chli, and R. Y. Siegwart, “BRISK: Binary robust invariant scalable keypoints,” in Proc. of 2011 International Conf. on Computer Vision, Barcelona, Spain, November 6–13, 2011, pp. 2548–2555.
H. J. Chien, C. C. Chuang, C. Y. Chen, and R. Klette, “When to use what feature? SIFT, SURF, ORB, or A-KAZE features for monocular visual odometry,” in Proc. of 2016 International Conf. on Image and Vision Computing New Zealand, Palmerston North, New Zealand, November 21–22, 2016, pp. 1–6.
O. Déniz, G. Bueno, J. Salido, and F. De la Torre, “Face recognition using histograms of oriented gradients,” Pattern Recognit Lett., vol. 32, no. 12, pp. 1598–1603, 2011.
C. Wang , “MTLDesc: Looking wider to describe better,” in Proceedings of the Thirty-Sixth Virtual AAAI Conference on Artificial Intelligence, February 22 – March 1, 2022, pp. 2388–2396.
M. Ibrahim, O. El-Gendy, and M. Farouk, “Distributed 3D object recognition system using smartphones,” in Proc. of ICIT The 7th International Conf. on Information Technology, Amman, Jordan, May 12–15, 2015, pp. 102–108.
N. H. Barnouti, S. S. M. Al-Dabbagh, W. E. Matti, and M. A. S. Naser, “Face detection and recognition using Viola-Jones with PCA-LDA and square Euclidean distance,” Int. J. Adv. Comput. Sci. Appl., vol. 7, no. 5, pp. 371–377, 2016.
M. H. Wan and Z. H. Lai, “Generalized discriminant local median preserving projections (GDLMPP) for face recognition,” Neural Process. Lett., vol. 49, pp. 951–963, 2019.
H. Nguyen-Quoc and V. T. Hoang, “Face recognition based on selection approach via canonical correlation analysis feature fusion,” in Proc. of Zooming Innovation in Consumer Technologies Conf., Novi Sad, Serbia, May 26–27, 2020, pp. 54–57.
J. Deng, J. Guo, J. Yang, A. Lattas, and S. Zafeiriou, “Variational prototype learning for deep face recognition,” in Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA, June 20–25, 2021, pp. 11906–11915.
B. Hicham, C. Ahmed, and L. Abdelali, “Face recognition method combining SVM machine learning and scale invariant feature transform,” in Proc. of E3S Web of Conf., vol. 351, 2022, Art no. 01033.
A. Eleyan and H. Demirel, “PCA and LDA based neural networks for human face recognition,” in Face Recognition, K. Delac and M. Grgic, Eds., Vienna, Austria: I-Tech, 2007, pp. 93–106.
F. S. Samaria and A. C. Harter, “Parameterization of a stochastic model for human face identification,” in Proc. of 1994 IEEE Workshop on Applications of Computer Vision, Sarasota, FL, USA, December 5–7, 1994, pp. 138–142.
S. Milborrow, J. Morkel, and F. Nicolls, “The MUCT landmarked face database,” Pattern Recognition Assoc. South Africa, vol. 201, pp. 1–6, 2010.
A. S. Georghiades, P. N. Belhumeur, and D. J. Kriegman, “From few to many: Illumination cone models for face recognition under variable lighting and pose,” IEEE Trans. Pattern Anal Mach Intell., vol. 23, no. 6, pp. 643–660, 2001.
T. S. Akheel, V. U. Shree, and S. A. Mastani, “Stochastic gradient descent linear collaborative discriminant regression classification based face recognition,” Evol. Intell., vol. 15, pp. 1729–1743, 2022.
E. D. Pisano, S. Zong, B. M. Hemminger, M. DeLuca, R. E. Johnston, K. Muller, M. P. Braeuning, and S. M. Pizer, “Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms,” J. Digit Imaging, vol. 11, 1998, Art no. 193.
E. M. Jawad, H. G. Daway, and H. J. Mohamad, “Retinal image enhancement by using adapted histogram equalization based on segmentation and lab color space,” Int. J. Intell. Eng. Syst., vol. 15, no. 3, pp. 614–622, 2022.
T. Ojala, M. Pietikäinen, and D. Harwood, “A comparative study of texture measures with classification based on featured distributions,” Pattern Recognit, vol. 29, no. 1, pp. 51–59, 1996.
G. Yue and L. Lu, “Face recognition based on improved BP neural network,” Proc. MATEC Web Conferences, vol. 139, 2017, Art no. 00063.
G. Yue and L. Lu, “Face recognition based on histogram equalization and convolution neural network,” in Proc. of 10th International Conf. on Intelligent Human-Machine Systems and Cybernetics, Hangzhou, China, August 25–26, 2018, vol. 1, pp. 336–339.
L. Goel, “A novel approach for face recognition using biogeography based optimization with extinction and evolution,” Multimed Tools Appl., vol. 81, no. 8, pp. 10561–10588, 2022.
F. Liu, Y. Ding, F. Xu, and Q. Ye, “Learning low-rank regularized generic representation with block-sparse structure for single sample face recognition,” IEEE Access, vol. 7, pp. 30573–30587, 2019.
L. Zhang, J. Liu, B. Zhang, D. Zhang, and C. Zhu, “Deep cascade model-based face recognition: When deep-layered learning meets small data,” IEEE Trans. Image Process., vol. 29, pp. 1016–1029, 2019.