Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{198985,
        author = {Amruta Netaji Taur and Prof Vijayshri A. Injamuri},
        title = {Advancing Facial Feature Recognition: A Comprehensive Review of Deep Learning, Challenges, and Future Directions},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {13137-13152},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198985},
        abstract = {Facial feature recognition has emerged as a critical component in numerous applications, including biometric authentication, surveillance, healthcare diagnostics, and human–computer interaction. With the rapid advancement of Artificial Intelligence, particularly deep learning, significant improvements have been achieved in the accuracy and efficiency of facial analysis systems. This paper presents a comprehensive review of state-of-the-art techniques for facial feature recognition, focusing on traditional machine learning approaches as well as modern deep learning architectures such as Convolutional Neural Networks (CNNs), transfer learning models, and attention-based frameworks. The study critically analyzes various stages of the recognition pipeline, including preprocessing, feature extraction, and classification, while highlighting the strengths and limitations of each method. In addition, this review explores key challenges faced by existing systems, such as variations in illumination, pose, occlusion, and demographic bias, which impact model generalization and fairness. Special emphasis is placed on emerging trends aiming to enhance transparency and trustworthiness in facial recognition models. Furthermore, the paper discusses issues related to privacy, security, and ethical considerations associated with the deployment of such technologies. A comparative analysis of recent methodologies is presented to provide insights into performance trade-offs between accuracy, computational complexity, and interpretability. Finally, the review outlines future research directions, emphasizing the need for robust, unbiased, and explainable facial feature recognition systems that can be reliably deployed in real-world scenarios.},
        keywords = {Facial Feature Recognition, Deep Learning, Convolutional Neural Networks, Explainable Artificial Facial Feature Recognition, Deep Learning, Convolutional Neural Networks, Explainable Artificial},
        month = {April},
        }