A Comprehensive Review of Machine Learning Algorithms and Datasets for Robust Face Recognition
Keywords:
Machine Learning, Image Processing, Face Recognition, Face Detection
Abstract
Face recognition has been established as an essential biometric technology in security, surveillance, and human-computer interaction systems for verifying identities. Despite blistering development, the current literature is frequently unable to provide a cohesive comparative study that integrates classical machine learning approaches, recent deep learning models, benchmark datasets, and evaluation procedures within a unified framework. This paper shows a qualitative and analytical review of the face recognition methods, including the entire pipeline, such as face detector and alignment, feature extraction, feature classification, and recognition with deep learning. The gap of the research covered by the present study consists in the fact that a unified assessment, quantitatively comparing the traditional algorithms (PCA, LDA, LBP, SVM) to the deep learning models (CNN-based methods) under the influence of different real-world factors, is missing. The originality of this work is threefold: (i) the single taxonomy of face recognition development phases, (ii) the comparative performance analysis of the databases on a dataset basis using normalized measures of accuracy, and (iii) the detailed discussion of practical problems that include illumination variation, occlusion, age effects, and image resolution. Measured contributions can be made as a quantitative comparison across 4 benchmark datasets (AR, AT&T, Yale, FERET), showing that CNN-based models consistently achieve higher recognition accuracy (up to 99.79) than classical methods. The review presents researchers and practitioners with practical information on the choice of face recognition models that can be used in the real-world context.
References
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[2] S. Ashraf, S. Saleem, T. Ahmed, Z. Aslam, and M. Shuaeeb, "Iris and foot based sustainable biometric identification approach," in 2020 International Conference on Software, Telecommunications and Computer Networks (SoftCOM). IEEE, 2020, pp. 1-6.
[3] Y. Kortli, M. Jridi, A. Al Falou, and M. Atri, "Face recognition systems: A survey," Sensors, vol. 20, no. 2, p. 342, 2020.
[4] P. Viola and M. J. Jones, "Robust real-time face detection," International journal of computer vision, vol. 57, pp. 137-154, 2004.
[5] B. Wu, H. Ai, C. Huang, and S. Lao, "Fast rotation invariant multi-view face detection based on real adaboost," in Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. IEEE, pp. 79-84.
[6] Y. Li, H. Ai, T. Yamashita, S. Lao, and M. Kawade, "Tracking in low frame rate video: A cascade particle filter with discriminative observers," IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 10, pp. 1728-1740, 2008.
[7] T. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham, "Active shape models-their training and application," Computer vision and image understanding, vol. 61, no. 1, pp. 38-59, 1995.
[8] D. Cristinacce and T. F. Cootes, "Boosted regression active shape models," in BMVC, vol. 2, 2007, pp. 880-889.
[9] X. Cao, Y. Wei, F. Wen, and J. Sun, "Face alignment by explicit shape regression," International journal of computer vision, vol. 107, pp. 177-190, 2014.
[10] W. Zhang et al., "Local gabor binary pattern histogram sequence (lgbphs): a novel non-statistical model," in Tenth IEEE ICCV, vol. 1. IEEE, 2005, pp. 786-791.
[11] D. Chen, X. Cao, F. Wen, and J. Sun, "Blessing of dimensionality: High-dimensional feature and its efficient compression for face verification," in CVPR, 2013, pp. 3025-3032.
[12] M. Turk and A. Pentland, "Eigenfaces for recognition," Journal of cognitive neuroscience, vol. 3, no. 1, pp. 71-86, 1991.
[13] M. O. Oloyede, G. P. Hancke, and H. C. Myburgh, "A review on face recognition systems: recent approaches and challenges," Multimedia Tools and Applications, vol. 79, no. 37, pp. 27891-27922, 2020.
[14] M. A. Turk and A. P. Pentland, "Face recognition using eigenfaces," in Proc. 1991 IEEE CVPR. IEEE, 1991, pp. 586-587.
[15] K. Huang, "Principal component analysis in the eigenface technique for facial recognition," 2012.
[16] I. Dagher, "Incremental pca-lda algorithm," in 2010 IEEE Int. Conf. Computational Intelligence. IEEE, 2010, pp. 97-101.
[17] H. R. Yazdani and A. R. Shojaeifard, "Facial recognition system using eigenfaces and pca," Mathematics and Computational Sciences, vol. 4, no. 1, pp. 29-35, 2023.
[18] S. Wang, J. Lu, X. Gu, H. Du, and J. Yang, "Semi-supervised linear discriminant analysis for dimension reduction and classification," Pattern Recognition, vol. 57, pp. 179-189, 2016.
[19] X. Wang and K. K. Paliwal, "Feature extraction and dimensionality reduction algorithms and their applications in vowel recognition," Pattern recognition, vol. 36, no. 10, pp. 2429-2439, 2003.
[20] A. Kaur and T. Sarabjit Singh, "Face recognition using pca and lda techniques," Int. Journal of Advanced Research in Computer and Communication Engineering, vol. 4, no. 3, pp. 308-310, 2015.
[21] N. Tyagi, "Introduction to linear discriminant analysis in supervised learning," analyticssteps.com, Mar 2021.
[22] D. Huang et al., "Local binary patterns and its application to facial image analysis: a survey," IEEE Trans. Syst. Man Cybern., vol. 41, no. 6, pp. 765-781, 2011.
[23] T. Ojala, M. Pietikainen, and D. Harwood, "A comparative study of texture measures with classification based on featured distributions," Pattern Recognit., vol. 29, pp. 51-59, 1996.
[24] C. Zhu, C.-E. Bichot, and L. Chen, "Multi-scale color local binary patterns for visual object classes recognition," in 20th ICPR. IEEE, 2010, pp. 3065-3068.
[25] T. Ojala, M. Pietikainen, and T. Maenpaa, "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns," IEEE Trans. PAMI, vol. 24, no. 7, pp. 971-987, 2002.
[26] S. Liao and A. C. Chung, "Face recognition by using elongated local binary patterns," in ACCV 2007. Springer, 2007, pp. 672-679.
[27] H. Jin, Q. Liu, H. Lu, and X. Tong, "Face detection using improved lbp under bayesian framework," in Third ICIG. IEEE, 2004, pp. 306-309.
[28] A. Petpon and S. Srisuk, "Face recognition with local line binary pattern," in 2009 Fifth ICIG. IEEE, 2009, pp. 533-539.
[29] X. Tan and B. Triggs, "Enhanced local texture feature sets for face recognition under difficult lighting conditions," in AMFG 2007. Springer, 2007, pp. 168-182.
[30] S. Liao et al., "Learning multi-scale block local binary patterns for face recognition," in ICB 2007. Springer, 2007, pp. 828-837.
[31] L. Wolf, T. Hassner, and Y. Taigman, "Descriptor based methods in the wild," in Workshop on faces in real-life images, 2008.
[32] C. Shan and T. Gritti, "Learning discriminative lbp-histogram bins for facial expression recognition," in BMVC, 2008, pp. 1-10.
[33] S. Ghosh, A. Dasgupta, and A. Swetapadma, "A study on support vector machine based linear and non-linear pattern classification," in 2019 ICISS. IEEE, 2019, pp. 24-28.
[34] F. Peter and S. John, "Support vector machine with radial basis function for facial emotion valence recognition," European Journal of Molecular & Clinical Medicine, vol. 9, no. 4, pp. 1960-1969, 2022.
[35] S. Mishra, "Breaking down the support vector machine (svm) algorithm," towardsdatascience.com, Oct 2020.
[36] Z. Li and X. Tang, "Bayesian face recognition using support vector machine and face clustering," in CVPR 2004, vol. 2. IEEE, 2004.
[37] M. I. Razzak, S. Naz, and A. Zaib, "Deep learning for medical image processing: Overview, challenges and the future," Classification in BioApps, pp. 323-350, 2018.
[38] N. Mahat et al., "Artificial neural network (ann) to predict mathematics students' performance," Journal of Computing Research and Innovation, vol. 7, pp. 29-38, 2022.
[39] H. Wang, R. Czerminski, and A. C. Jamieson, "Neural networks and deep learning," in The machine age of customer insight. Emerald, 2021.
[40] D. Svozil, V. Kvasnicka, and J. Pospichal, "Introduction to multi-layer feed-forward neural networks," Chemometrics and intelligent laboratory systems, vol. 39, no. 1, pp. 43-62, 1997.
[41] C. Wang and L. Wang, "Artificial neural network and its application in image recognition," Journal of Engineering Research and Reports, vol. 24, no. 2, pp. 50-57, 2023.
[42] A. Kumar, "Different types of cnn architectures explained: Examples," vitalflux.com, Apr 2022.
[43] B. R. Ilyas et al., "Facial expression recognition based on dwt feature for deep cnn," in 2019 CoDIT. IEEE, 2019, pp. 344-348.
[44] K. Patel, "Convolutional neural networks: a beginner's guide," towardsdatascience.com, Sep 2019.
[45] N. Aljaafari, "Ichthyoplankton classification tool using generative adversarial networks and transfer learning," Ph.D. dissertation, 2018.
[46] T. KIDS, "Convolutional neural networks: a beginner's guide," medium.com, Jul 2019.
[47] J. Gao, L. Li, and B. Guo, "A new extendface representation method for face recognition," Neural Processing Letters, vol. 51, no. 1, pp. 473-486, 2020.
[48] E. Khoshnevisan, H. Hassanpour, and M. M. AlyanNezhadi, "Face recognition based on general structure and angular face elements," Multimedia Tools and Applications, pp. 1-19, 2024.
[49] Y. Zhu and Y. Jiang, "Optimization of face recognition algorithm based on deep learning multi feature fusion," Image and Vision Computing, vol. 104, p. 104023, 2020.
[50] M. Norouzi et al., "A survey on face recognition based on deep neural networks," 2022.
[51] F. Liu et al., "Deep learning based single sample face recognition: a survey," Artificial Intelligence Review, vol. 56, no. 3, pp. 2723-2748, 2023.
[52] A. Ouyang et al., "A hybrid improved kernel lda and pnn algorithm for efficient face recognition," Neurocomputing, vol. 393, pp. 214-222, 2020.
[53] M. M. Ahsan et al., "Evaluating the performance of eigenface, fisherface, and lbph-based facial recognition methods," Technologies, vol. 9, no. 2, p. 31, 2021.
[54] T. Tuncer et al., "A novel facial image recognition method based on perceptual hash using quintet triple binary pattern," Multimedia Tools and Applications, vol. 79, no. 39, pp. 29573-29593, 2020.
[55] F. L. Abbasi et al., "Cnn-based face detection focusing on diverse visual variations," in Navigating Challenges of Object Detection. IGI Global, 2025, pp. 63-96.
[56] S. M. Daniyal et al., "An improved face recognition method based on convolutional neural network," Journal of Independent Studies and Research Computing, vol. 22, no. 1, pp. 103-110, 2024.
[57] J. Huang et al., "An mpca/lda based dimensionality reduction algorithm for face recognition."
[58] L. Zhou, M. Gao, and C. He, "Study on face recognition under unconstrained conditions based on lbp and deep learning," Journal of computational methods, vol. 21, no. 2, pp. 497-508, 2021.
[59] A. Aggarwal et al., "Principal component analysis, hidden markov model, and ann inspired techniques to recognize faces," Concurrency and Computation, vol. 33, no. 9, p. e6157, 2021.
[60] A. Hattab and A. Behloul, "An illumination-robust face recognition approach based on cnn," in Int. Symposium on Modelling. Springer, 2022, pp. 135-149.
[61] T. Chen et al., "A novel face recognition method based on fusion of lbp and hog," IET Image Processing, vol. 15, no. 14, pp. 3559-3572, 2021.
[62] Z. Wang et al., "Mmpcanet: An improved pcanet for occluded face recognition," Applied Sciences, vol. 12, no. 6, p. 3144, 2022.
[63] M. A. Yaman, F. Rattay, and A. Subasi, "Comparison of bagging and boosting ensemble machine learning methods for face recognition," Procedia Computer Science, vol. 194, pp. 202-209, 2021.
[64] M. A. BERBAR, "Faces recognition and facial gender classification using convolutional neural network," Menoufia Journal of Electronic Engineering Research, vol. 31, no. 2, pp. 1-10, 2022.
Published
2026-07-31
How to Cite
Daniyal, S., Abbasi, M., Saud, S., Zahid, N., Abbas, S., & Amjad, U. (2026). A Comprehensive Review of Machine Learning Algorithms and Datasets for Robust Face Recognition. International Journal of Computing and Related Technologies, 5(2), 1-14. Retrieved from https://ijcrt.smiu.edu.pk/index.php/smiu/article/view/304
Section
Articles
Copyright (c) 2026 Syed Muhammad Daniyal, Mohsin Mubeen Abbasi, Sarmad Saud, Noman Bin Zahid, Sahar Abbas, Usama Amjad
This work is licensed under a Creative Commons Attribution 4.0 International License.
