A Comprehensive Review of 1D Deep Learning Approaches in Facial Analysis: Face Recognition, Landmark Detection, and Mesh Modeling

Facial Analysis has progressed rapidly with deep learning and its 2D image-based models, especially Convolutional Neural Networks (CNNs), which have been the most popular methods. In recent years, 1D deep learning models have gained traction in the search for efficient solutions for face recognition, facial landmark detection, and 3D face mesh modeling. 1D models encode the facial structure as sequences, curves, or temporal signals, resulting in high computational efficiency, a small memory footprint, and good interpretability, making them well-suited for real-time and edge devices. This review is a step-by-step, organized exploration of 1D deep learning analysis of the face, its strengths and weaknesses, and the implementation trade-offs. A comprehensive classification of 1D facial representations has been proposed, along with evaluations of core architectures, comparisons with standardized benchmarks, and an overview of deployment in the field, demographic fairness, and privacy. Finally, a new unified evaluation framework has been suggested to provide a fair and replicable benchmark of 1D facial analysis models.