姿势引导的级联回归：面部标志点跟踪的鲁棒解决方案

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本文主要探讨了"通过级联回归进行稳健的面部界标跟踪"这一主题，发表在知名期刊《模式识别》(PatternRecognition)上，该期刊的网址是www.elsevier.com/locate/pr。研究的作者是Qingshan Liu、Jing Yang、Jiankang Deng和Kaihua Zhang，他们来自中国南京信息科技学院的大数据分析技术重点实验室。当前，尽管静态图像中的面部地标定位已经取得了显著的进步，但在连续视频中检测和跟踪面部形状仍然是一个具有挑战性的问题。由于视频中面部表情、姿势和光照条件的变化，要求跟踪系统必须具备良好的鲁棒性。针对这个问题，论文提出了一种基于级联回归的面部地标跟踪系统，该方法特别关注处理序列图像中出现的一些挑战。该系统的核心是采用一种基于姿态的级联形状回归模型来预测面部地标的位置。这种模型利用姿态信息减小了学习阶段的形状变化，从而使得学习到的回归模型更加稳定和适应性更强。姿态信息的考虑有助于减少因头部运动导致的地标位置预测误差，提高了在动态环境下的跟踪精度。论文的关键词包括：面部检测、面部对齐、面部跟踪、级联回归。通过对级联回归算法的优化，研究者旨在构建一个能够在不同场景和条件中保持高效且准确的面部地标跟踪解决方案，这对于人脸识别、动画捕捉以及视频分析等领域具有重要意义。这篇研究论文提供了一种创新的方法，将级联回归与姿态信息相结合，为解决实时和动态环境下面部地标跟踪的难题提供了新的思路和技术支持。通过这种方法，研究人员期望能够改善现有系统的性能，并推动人脸检测和跟踪技术在实际应用中的进一步发展。

Contents lists available at ScienceDirect

Pattern Recognition

journal homepage: www.elsevier.com/locate/pr

Robust facial landmark tracking via cascade regression

Qingshan Liu, Jing Yang, Jiankang Deng, Kaihua Zhang

⁎

Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing, China

ARTICLE INFO

Keywords:

Face detection

Face alignment

Face tracking

Cascade regression

ABSTRACT

Recently, tremendous improvements have been achieved for facial landmark localization on static images.

However, detecting and tracking facial shapes in sequential images is still challenging due to the large

appearance variations in unconstrained videos. To address this issue, we present a robust facial landmark

tracking system via cascade regression, which is able to deal well with some challenges emerging in the

sequential images. Specially, our system employs a pose-based cascade shape regression model to predict the

facial landmark locations. Pose-based cascade shape regression model decreases the shape variances in the

model learning stage, making the learned regression model more robust to the large pose variances. In addition,

we explore a pose tracking model to enhance the temporal consecutiveness between the adjacent frames, and

leverage the Kalman ﬁlter to make the predicted shape more smooth and stable. Finally, we incorporate a re-

initialization mechanism with the facial landmarks as the position priors into the system, which is able to

eﬀectively and accurately locate the face when it is misaligned or lost. Experiments on the LFPW, Helen, 300 W

and 300 VW datasets illustrate the superiority of proposed system over the state-of-the-art approaches, and it is

worthy emphasizing that our method has won the 300 VW competition in the category one.

1. Introduction

Facial landmark localization is among the most popular and well-

studied problems in the domain of computer vision [1] with a wide

range of applications, such as facial attribute analysis [2], face

veriﬁcation [3–5], and face segmentation, tracking and recognition

[6–14], to name a few. To design a robust facial landmark localization

system is a great challenge due to extensive rigid and non-rigid face

variations, as along with unconstrained imaging conditions such as

illumination changes and occlusions in the real world conditions. In the

past two decades, numerous algorithms have been proposed [15,16] for

facial landmark localization, which can be roughly categorized into two

major categories: generative methods and discriminative methods.

Generative methods typically optimize the shape parameters itera-

tively with the purpose of best approximately reconstructing an input

image by a facial deformable model. Active Shape Models (ASMs) [17–

20] and Active Appearance Models (AAMs) [21–26] are two typical

representatives. In the ASMs, a global shape is constructed by applying

the Principal Component Analysis (PCA) method to the aligned

training set, and then the appearance is modeled partially with the

discriminatively learned templates. In the AAMs, the shape model

shares the same point distribution with that in the ASMs, while the

global appearance is modeled by PCA after removing shape variation in

the canonical coordinate frame.

Discriminative methods attempt to infer a face shape through a

discriminative regression function by directly mapping textual features

to the shapes. In [27], a cascaded regression method built on pose-

index feature has been introduced to pose estimation with excellent

performance. Cao et al. [28] integrate a two-level boosted regression

framework, shape-indexed features and a valid feature selection

method to make the regression more eﬀective and eﬃcient. Xiong

et al. [29] concatenate the SIFT features of each landmark as the

feature representation and obtain a regression matrix via linear

regression. In [30], a learning strategy is devised for a cascaded

regression approach by considering the structure of the problem.

Despite the demonstrated success of facial landmark localization in

the static images, less attention has been paid to facial landmark

tracking in the lengthy videos [31– 33] due to the challenging factors

such as expression, illumination, occlusion, pose, image quality en-

countered in unconstrained videos, and the lack of designed bench-

mark. Fortunately, the 300 VW challenge [34] has presented a new

comprehensive benchmark recently which covers faces in the uncon-

strained environments, under the various lighting conditions, in the

arbitrary expressions and possibly occluded by the other objects.

This paper is an extension of our previous work that was accepted

by ICCVW 2015 [35]. The main contributions of this paper are

following. In this paper, we construct a novel system based on cascade

regression for facial landmark tracking, and the main idea of which is

http://dx.doi.org/10.1016/j.patcog.2016.12.024

Received 15 July 2016; Received in revised form 22 December 2016; Accepted 22 December 2016

⁎

Corresponding author.

E-mail address: zhkhua@gmail.com (K. Zhang).

Pattern Recognition (xxxx) xxxx–xxxx

Please cite this article as: Liu, Q., Pattern Recognition (2016), http://dx.doi.org/10.1016/j.patcog.2016.12.024

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