Facial Landmark Detection: a Literature Survey

TL;DR

This literature survey comprehensively reviews facial landmark detection algorithms, categorizing them into holistic, CLM, and regression-based methods, comparing their performance, and discussing future research directions including deep learning approaches.

Contribution

It provides an extensive classification, comparison, and analysis of existing facial landmark detection methods, including recent deep learning techniques, and suggests future research directions.

Findings

Holistic methods model global facial appearance and shape.

CLM methods combine global shape models with local appearance.

Regression-based methods implicitly learn facial features.

Abstract

The locations of the fiducial facial landmark points around facial components and facial contour capture the rigid and non-rigid facial deformations due to head movements and facial expressions. They are hence important for various facial analysis tasks. Many facial landmark detection algorithms have been developed to automatically detect those key points over the years, and in this paper, we perform an extensive review of them. We classify the facial landmark detection algorithms into three major categories: holistic methods, Constrained Local Model (CLM) methods, and the regression-based methods. They differ in the ways to utilize the facial appearance and shape information. The holistic methods explicitly build models to represent the global facial appearance and shape information. The CLMs explicitly leverage the global shape model but build the local appearance models. The regression-based methods implicitly capture facial shape and appearance information. For algorithms within each category, we discuss their underlying theories as well as their differences. We also compare their performances on both controlled and in the wild benchmark datasets, under varying facial expressions, head poses, and occlusion. Based on the evaluations, we point out their respective strengths and weaknesses. There is also a separate section to review the latest deep learning-based algorithms. The survey also includes a listing of the benchmark databases and existing software. Finally, we identify future research directions, including combining methods in different categories to leverage their respective strengths to solve landmark detection "in-the-wild".