The Development and Challenges of Face Alignment Algorithms

Abstract

A comprehensive survey of face alignment using different methods is presented in this paper. Face alignment is the fundamental task of facial applications, e.g., face recognition, 3D face modelling and face expression analysis, etc. State-of-the-art methods can be mainly categorized into the three groups: gradient descent-based, deep learning-based, and 3D model-based. In gradient descent-based methods, landmarks are localized and adjusted by solving a nonlinear regression function. Deep learning-based methods construct one or several cascaded neural networks to improve landmark localization accuracy. Beside the above two categories of methods solving problems on a 2D plane, there is also other category of methods like 3D model-based methods. Despite significant progress that has been made, face alignment faces challenges from real-world conditions: variation across poses, genders and ages, facial expressions, and facial attributes. This paper offers a brief illustration and analysis of several typical methods of face alignment, provides an overall understanding and insight into the field, which will motivate us to explore promising future directions.