Facial Feature Point Detection: A Comprehensive Survey

TL;DR

This survey comprehensively reviews facial feature point detection methods, categorizing them into major groups, analyzing their strengths and limitations, and discussing future research directions in real-world conditions.

Contribution

It provides a detailed classification and comparison of existing facial feature point detection techniques, highlighting challenges and potential future research avenues.

Findings

Significant progress has been made in facial feature detection.

Current methods face challenges under wild and real-world conditions.

Deep learning-based methods are emerging as promising approaches.

Abstract

This paper presents a comprehensive survey of facial feature point detection with the assistance of abundant manually labeled images. Facial feature point detection favors many applications such as face recognition, animation, tracking, hallucination, expression analysis and 3D face modeling. Existing methods can be categorized into the following four groups: constrained local model (CLM)-based, active appearance model (AAM)-based, regression-based, and other methods. CLM-based methods consist of a shape model and a number of local experts, each of which is utilized to detect a facial feature point. AAM-based methods fit a shape model to an image by minimizing texture synthesis errors. Regression-based methods directly learn a mapping function from facial image appearance to facial feature points. Besides the above three major categories of methods, there are also minor categories of methods which we classify into other methods: graphical model-based methods, joint face alignment methods, independent facial feature point detectors, and deep learning-based methods. Though significant progress has been made, facial feature point detection is limited in its success by wild and real-world conditions: variations across poses, expressions, illuminations, and occlusions. A comparative illustration and analysis of representative methods provide us a holistic understanding and deep insight into facial feature point detection, which also motivates us to explore promising future directions.