Systematic Review: Recent Advancements in Deep Learning Techniques for Facial Feature Recognition
DOI:
https://doi.org/10.56294/dm2025658Keywords:
Convolutional Neural Networks, Long Short-Term Memory Networks, Generative Adversarial Networks, Hybrid Deep Learning Approaches, Human-Computer Interaction, Multi-modal SystemsAbstract
Deep Learning is a rapidly evolving field with critical contributions to various domains including security, healthcare, and human — computer interaction, etc. It reviews the significant developments in the area of facial recognition using deep learning techniques. It explains deep learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), and Generative Adversarial Networks (GANs), as well as hybrid models and transfer learning uses. It also addresses technical, ethical, and legal challenges that arise for facial analysis systems and emphasizes the need for real-time processing, multi-modal systems, and robust algorithms to improve the technical accuracy and fairness of facial analysis.
References
Albiero V, Chen X, Yin X, Pang G, Hassner T. img2pose: Face alignment and detection via 6dof, face pose estimation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition 2021:7617-7627. DOI: https://doi.org/10.1109/CVPR46437.2021.00753
Wu Y, Ji Q. Facial landmark detection: A literature survey. International Journal of Computer Vision. 2019; 127:115-42. DOI: https://doi.org/10.1007/s11263-018-1097-z
Ji W, Jin L. Face Shape Classification Based on MTCNN and FaceNet. In 2021 2nd International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI) 2021:167-170. IEEE. DOI: https://doi.org/10.1109/ICHCI54629.2021.00042
Al-Bander B, Alzahrani T, Alzahrani S, Williams BM, Zheng Y. Improving fetal head contour detection by object localisation with deep learning. In Annual Conference on Medical Image Understanding and Analysis 2019:142-150. Cham: Springer International Publishing. DOI: https://doi.org/10.1007/978-3-030-39343-4_12
Saichandran KS. Hand Shape and Gesture Recognition using Multi-scale Template Matching, Background Subtraction and Binary Image Analysis. arXiv preprint arXiv:2402.09663. 2024.
Hossam M, Afify AA, Rady M, Nabil M, Moussa K, Yousri R, Darweesh MS. A comparative study of different face shape classification techniques. In 2021 International Conference on Electronic Engineering (ICEEM) 2021:1-6. IEEE. DOI: https://doi.org/10.1109/ICEEM52022.2021.9480638
Alzahrani T, Al-Nuaimy W, Al-Bander B. Hybrid feature learning and engineering based approach for face shape classification. In 2019 International Conference on Intelligent Systems and Advanced Computing Sciences (ISACS) 2019:1-4. IEEE. DOI: https://doi.org/10.1109/ISACS48493.2019.9068910
Tio AE. Face shape classification using inception v3. arXiv preprint arXiv:1911.07916. 2019.
Marinescu AI, Ileni TA, Darabant AS. A versatile 3d face reconstruction from multiple images for face shape classification. In 2019 International Conference on Software, Telecommunications and Computer Networks (SoftCOM) 2019:1-6. IEEE. DOI: https://doi.org/10.23919/SOFTCOM.2019.8903745
Razalli H, Rahmat RW, Khalid F, Sulaiman PS. An Image-Based Children Age Range Verification and Classification Based on Facial Features Angle Distribution and Face Shape Elliptical Ratio. Advanced Science Letters. 2017; 23(5):4026-30. DOI: https://doi.org/10.1166/asl.2017.8271
Deng J, Trigeorgis G, Zhou Y, Zafeiriou S. Joint multi-view face alignment in the wild. IEEE Transactions on Image Processing. 2019; 28(7):3636-48. DOI: https://doi.org/10.1109/TIP.2019.2899267
Nabil M, Rady M, Moussa K, Wessam M, Hossam M, Yousri R, Darweesh MS. A pre-processing approach to improve the performance of inception v3-based face shape classification. In 2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES) 2021:205-209. IEEE. DOI: https://doi.org/10.1109/NILES53778.2021.9600535
Sabir E, Cheng J, Jaiswal A, AbdAlmageed W, Masi I, Natarajan P. Recurrent convolutional strategies for face manipulation detection in videos. Interfaces (GUI). 2019; 3(1):80-7.
Abdullah, Hussain A, Ali S, Kim HC, Sain M, Aich S. Hybrid Based Model Face Shape Classification Using Ensemble Method for Hairstyle Recommender System. In International conference on smart computing and cyber security: strategic foresight, security challenges and innovation 2021:61-68. Singapore: Springer Nature Singapore. DOI: https://doi.org/10.1007/978-981-16-9480-6_7
Polat S, Kabakc A, Cevik Y, YÜCEL A. The Face Shape and Golden Ratio Classification in Turkish Healthy Population. Journal of Evolution of Medical and Dental Sciences-Jemds. 2020; 9(2). DOI: https://doi.org/10.14260/jemds/2020/24
Zhou D, Jin X, Jiang Q, Cai L, Lee SJ, Yao S. MCRD‐Net: An unsupervised dense network with multi‐scale convolutional block attention for multi‐focus image fusion. IET Image Processing. 2022; 16(6):1558-74. DOI: https://doi.org/10.1049/ipr2.12430
Thevenot J, López MB, Hadid A. A survey on computer vision for assistive medical diagnosis from faces. IEEE journal of biomedical and health informatics. 2017; 22(5):1497-511. DOI: https://doi.org/10.1109/JBHI.2017.2754861
Rahmat RF, Syahputra MD, Andayani U, Lini TZ. Probabilistic neural network and invariant moments for men face shape classification. In IOP Conference Series: Materials Science and Engineering 2018; 420(1):012095. IOP Publishing.
Ross A, Othman A. Visual cryptography for biometric privacy. IEEE transactions on information forensics and security. 2010; 6(1):70-81. DOI: https://doi.org/10.1109/TIFS.2010.2097252
Rossler A, Cozzolino D, Verdoliva L, Riess C, Thies J, Nießner M. Faceforensics++: Learning to detect manipulated facial images. In Proceedings of the IEEE/CVF international conference on computer vision 2019:1-11. DOI: https://doi.org/10.1109/ICCV.2019.00009
Mehta A, Mahmoud T. Human Face Shape Classification with Machine Learning.
Sukumaran A, Brindha T. Optimal feature selection with hybrid classification for automatic face shape classification using fitness sorted Grey wolf update. Multimedia Tools and Applications. 2021; 80:25689-710. DOI: https://doi.org/10.1007/s11042-021-10710-9
Alzahrani T, Al-Nuaimy W, Al-Bander B. Integrated multi-model face shape and eye attributes identification for hair style and eyelashes recommendation. Computation. 2021; 9(5):54. DOI: https://doi.org/10.3390/computation9050054
Zhao J, Zhang M, He C, Xie X, Li J. A novel facial attractiveness evaluation system based on face shape, facial structure features and skin. Cognitive Neurodynamics. 2020; 14:643-56. DOI: https://doi.org/10.1007/s11571-020-09591-9
Shao Z, Liu Z, Cai J, Ma L. Jaa-net: joint facial action unit detection and face alignment via adaptive attention. International Journal of Computer Vision. 2021; 129:321-40. DOI: https://doi.org/10.1007/s11263-020-01378-z
Adityatama R, Putra AT. Image classification of Human Face Shapes Using Convolutional Neural Network Xception Architecture with Transfer Learning. Recursive Journal of Informatics. 2023; 1(2):102-9. DOI: https://doi.org/10.15294/rji.v1i2.70774
Salim BV, Indrawan JO, Hidayat J, Matthew S, Mangkang TA, Hasana S, Permonangan IH. Face Shape Classification Using Swin Transformer Model. Procedia Computer Science. 2023; 227:557-62.
VenkateswarLal P, Nitta GR, Prasad A. Ensemble of texture and shape descriptors using support vector machine classification for face recognition. Journal of Ambient Intelligence and Humanized Computing. 2019:1-8. DOI: https://doi.org/10.1007/s12652-019-01192-7
Radhika R, Mahajan R. An adaptive optimum weighted mean filter and bilateral filter for noise removal in cardiac MRI images. Measurement: Sensors. 2023; 29:100880. DOI: https://doi.org/10.1016/j.measen.2023.100880
Rajani Kumari LV, Saher Fathima S, Sai Praneeth G, Mamatha D, Pranitha B. Dynamic face recognition system using histogram of oriented gradients and deep neural network. InSustainable Communication Networks and Application: Proceedings of ICSCN 2021 2022 (pp. 229-241). Singapore: Springer Nature Singapore. DOI: https://doi.org/10.1007/978-981-16-6605-6_16
Goodfellow, I., et al. (2014). Generative Adversarial Networks. Advances in Neural Information Processing Systems.
Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780. DOI: https://doi.org/10.1162/neco.1997.9.8.1735
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems.
LeCun, Y., et al. (1998). Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE, 86(11), 2278-2324. DOI: https://doi.org/10.1109/5.726791
Schroff, F., Kalenichenko, D., & Philbin, J. (2015). FaceNet: A Unified Embedding for Face Recognition and Clustering. IEEE Conference on Computer Vision and Pattern Recognition. DOI: https://doi.org/10.1109/CVPR.2015.7298682
Srinivas, A. & Vamsidhar, E., (2025). MML-FuseNet: An Efficient Multi-Task Mutual Learning Framework for Face Shape Detection Based on Confined Feature Fusion. Computational Intelligence.
Srinivas, A. & Vamsidhar, E., (2025). Deep Learning based Face Shape Classification System with Binary Feature Selection Model. Expert Systems with Applications.
Srinivas, A. & Vamsidhar, E., (2023). Multimodal face shape detection based on Human Temperament with Hybrid Feature Fusion and Inception V3 Extraction Model. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization.
Zafar, A., & Popa, T. (2016). Face and eye-ware classification using geometric features for a data-driven eye-ware recommendation system. In Graphics Interface Conference (pp. 183-188). New York, NY, USA: ACM Library.
Sarakon, P., Charoenpong, T., & Charoensiriwath, S. (2014, November). Face shape classification from 3D human data by using SVM. In The 7th 2014 Biomedical Engineering International Conference (pp. 1-5). IEEE. DOI: https://doi.org/10.1109/BMEiCON.2014.7017382
Bansode, N. K., & Sinha, P. K. (2016). Face shape classification based on region similarity, correlation and fractal dimensions. International Journal of Computer Science Issues (IJCSI), 13(1), 24. DOI: https://doi.org/10.20943/IJCSI-201602-2431
Sunhem, W., & Pasupa, K. (2016, February). An approach to face shape classification for hairstyle recommendation. In 2016 Eighth International Conference on Advanced Computational Intelligence (ICACI) (pp. 390-394). IEEE. DOI: https://doi.org/10.1109/ICACI.2016.7449857
Rahmat, R. F., Syahputra, M. D., Andayani, U., & Lini, T. Z. (2018, September). Probabilistic neural network and invariant moments for men face shape classification. In IOP Conference Series: Materials Science and Engineering (Vol. 420, No. 1, p. 012095). IOP Publishing. DOI: https://doi.org/10.1088/1757-899X/420/1/012095
Tio, A. E. (2019). Face shape classification using inception v3. arXiv preprint arXiv:1911.07916.
Pasupa, K., Sunhem, W., & Loo, C. K. (2019). A hybrid approach to building face shape classifier for hairstyle recommender system. Expert Systems with Applications, 120, 14-32. DOI: https://doi.org/10.1016/j.eswa.2018.11.011
Salim, B. V., Indrawan, J. O., Hidayat, J., Matthew, S., Mangkang, T. A. E., Hasana, S., & Permonangan, I. H. (2023). Face Shape Classification Using Swin Transformer Model. Procedia Computer Science, 227, 557-562. DOI: https://doi.org/10.1016/j.procs.2023.10.558
Rabea, M., Ahmed, H., Mahmoud, S., & Sayed, N. (2024). IdentiFace: A VGG Based Multimodal Facial Biometric System. arXiv preprint arXiv:2401.01227.
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