3D约束局部模型：刚性和非刚性面部跟踪

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"3D Constrained Local Model for Rigid and Non-Rigid Facial Tracking" 这篇论文主要介绍了一种3D约束局部模型（CLM-Z），用于在不同姿态下对人脸特征进行鲁棒跟踪。该方法结合了深度和强度信息，提供了一个共同的框架，以提高跟踪的准确性和收敛速度。 1. 引言人脸表情和头部姿势是人类互动中的关键信息源，提供了重要的沟通渠道。现有的面部跟踪技术往往难以处理面部的刚性和非刚性变形。CLM-Z方法解决了这一问题，它通过结合来自深度传感器的数据和传统的灰度图像信息，增强了对脸部特征变化的适应能力。 2. 方法 3D Constrained Local Model (CLM-Z) 是一种改进的局部模型，它引入了深度信息来增强对人脸特征的定位。CLM-Z能够在保持对刚性（如头部转动）和非刚性（如面部表情）变化的跟踪性能的同时，减少错误积累和漂移。通过深度信息，模型能够更好地理解面部的三维结构，从而在复杂的头部运动中保持稳定。 3. 实验与比较实验在公开数据集上验证了CLM-Z的优势，显示了其在跟踪精度和收敛速度上的提升，超过了常规的CLM方法。此外，作者还展示了一种将CLM-Z与刚性头部追踪器结合的方法，这进一步优化了对头部刚性运动的追踪性能。 4. 结果与讨论与当前最先进的头部位置追踪技术相比，CLM-Z的扩展——广义自适应视图基表现模型（GAVAM）在头姿追踪任务中表现出更好的性能。这表明，结合深度信息的局部模型对于复杂环境下的面部和头部跟踪具有显著优势。 5. 应用前景 CLM-Z的应用前景广泛，包括但不限于情感识别、人机交互、虚拟现实和监控系统。这种技术可以提升这些领域中对人脸和头部动态行为的理解和分析。 6. 结论 CLM-Z通过结合3D信息提高了面部特征跟踪的鲁棒性和精确性，尤其在处理头部的刚性和非刚性运动时效果显著。这一方法为未来的人脸识别和追踪技术提供了新的思路和工具。 3D约束局部模型CLM-Z是面部识别和追踪领域的一个重要进展，它创新地融合了深度信息，以应对面部在不同条件下的复杂变化，提升了追踪的准确性和实时性。

3D Constrained Local Model for Rigid and Non-Rigid Facial Tracking

Tadas Baltru

saitis Peter Robinson

University of Cambridge Computer Laboratory

15 JJ Thomson Avenue

tb346@cl.cam.ac.uk pr10@cl.cam.ac.uk

Louis-Philippe Morency

USC Institute for Creative Technologies

12015 Waterfront Drive

morency@ict.usc.edu

Abstract

We present 3D Constrained Local Model (CLM-Z) for

robust facial feature tracking under varying pose. Our ap-

proach integrates both depth and intensity information in

a common framework. We show the beneﬁt of our CLM-

Z method in both accuracy and convergence rates over

regular CLM formulation through experiments on publicly

available datasets. Additionally, we demonstrate a way to

combine a rigid head pose tracker with CLM-Z that beneﬁts

rigid head tracking. We show better performance than the

current state-of-the-art approaches in head pose tracking

with our extension of the generalised adaptive view-based

appearance model (GAVAM).

1. Introduction

Facial expression and head pose are rich sources of infor-

mation which provide an important communication chan-

nel for human interaction. Humans use them to reveal in-

tent, display affection, express emotion, and help regulate

turn-taking during conversation [1, 12]. Automated track-

ing and analysis of such visual cues would greatly bene-

ﬁt human computer interaction [22, 31]. A crucial initial

step in many affect sensing, face recognition, and human

behaviour understanding systems is the estimation of head

pose and detection of certain facial feature points such as

eyebrows, corners of eyes, and lips. Tracking these points

of interest allows us to analyse their structure and motion,

and helps with registration for appearance based analysis.

This is an interesting and still an unsolved problem in com-

puter vision. Current approaches still struggle in person-

independent landmark detection and in the presence of large

pose and lighting variations.

There have been many attempts of varying success at

tackling this problem, one of the most promising being

the Constrained Local Model (CLM) proposed by Cristi-

nacce and Cootes [10], and various extensions that fol-

lowed [18, 23, 27]. Recent advances in CLM ﬁtting and

response functions have shown good results in terms of ac-

Figure 1. Response maps of three patch experts: (A) face outline,

(B) nose ridge and (C) part of chin. Logistic regressor response

maps [23, 27] using intensity contain strong responses along the

edges, making it hard to ﬁnd the actual feature position. By inte-

grating response maps from both intensity and depth images, our

CLM-Z approach mitigates the aperture problem.

curacy and convergence rates in the task of person indepen-

dent facial feature tracking. However, they still struggle in

under poor lighting conditions.

In this paper, we present a 3D Constrained Local Model

(CLM-Z) that takes full advantage of both depth and in-

tensity information to detect facial features in images and

track them across video sequences. The use of depth data

allows our approach to mitigate the effect of lighting con-

ditions. In addition, it allows us to reduce the effects of the

aperture problem (see Figure 1), which arises because of

patch response being strong along the edges but not across

them. An additional advantage of our method is the option

to use depth only CLM responses when no intensity signal

is available or lighting conditions are inadequate.

Furthermore, we propose a new tracking paradigm which

integrates rigid and non-rigid facial tracking. This paradigm

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