Automated face analysis by feature point tracking has high concurrent validity with manual FACS coding
Automated face analysis by feature point tracking has high concurrent validity with manual FACS coding
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DOI：
10.1017/s0048577299971184
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发表时间：
1999-01-01
期刊：
影响因子：
3.7
通讯作者：
Kanade, T
中科院分区：
文献类型：
--
作者：
Cohn, JF;Zlochower, AJ;Kanade, T
面部是人类行为信息的丰富来源。然而，用于编码面部显示的可用方法依赖于人类观察者，劳动密集型，并且难以标准化。为了使严格和有效的定量测量的面部显示，我们已经开发了一种自动化的面部显示分析方法。在这份报告中，我们比较的结果与手动FAGS（面部动作编码系统，埃克曼和弗里森，1978年a）编码的自动化系统。100名大学生在进行一系列面部展示时被录像。图像序列由经认证的FAGS编码器从录像带编码。选择发生至少25次的15个动作单元和动作单元组合进行自动分析。面部特征自动跟踪数字化图像序列使用的层次算法估计光流。将测量值针对位置、方向和尺度的变化进行归一化。将图像序列随机分为训练集和交叉验证集，并对特征点测量进行判别函数分析。在训练集中，与手动FAGS编码的平均一致性为92%或更高的动作单位在眉毛，眼睛和嘴巴区域。在交叉验证集中，平均一致率分别为91%，88%和81%的行动单位在眉毛，眼睛和嘴部地区，分别。通过特征点跟踪的自动人脸分析与手工FAGS编码具有很高的并发有效性。
The face is a rich source of information about human behavior. Available methods for coding facial displays, however, are human-observer dependent, labor intensive, and difficult to standardize. To enable rigorous and efficient quantitative measurement of facial displays, we have developed an automated method of facial display analysis. In this report, we compare the results with this automated system with those of manual FAGS (Facial Action Coding System, Ekman & Friesen, 1978a) coding. One hundred university students were videotaped while performing a series of facial displays. The image sequences were coded from videotape by certified FAGS coders. Fifteen action units and action unit combinations that occurred a minimum of 25 times were selected for automated analysis. Facial features were automatically tracked in digitized image sequences using a hierarchical algorithm for estimating optical flow. The measurements were normalized for variation in position, orientation, and scale. The image sequences were randomly divided into a training set and a cross:validation set, and discriminant function analyses were conducted on the feature point measurements. In the training set, average agreement with manual FAGS coding was 92% or higher for action units in the brow, eye, and mouth regions. In the cross-validation set, average agreement was 91%, 88%, and 81% for action units in the brow, eye, and mouth regions, respectively. Automated face analysis by feature point tracking demonstrated high concurrent validity with manual FAGS coding.