Geometric feature-based facial expression recognition in image sequences using multi-class AdaBoost and support vector machines.
Geometric feature-based facial expression recognition in image sequences using multi-class AdaBoost and support vector machines.
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DOI：
10.3390/s130607714
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发表时间：
2013-06-14
期刊：
影响因子：
--
通讯作者：
Lee J
中科院分区：
文献类型：
--
作者：
Ghimire D;Lee J
面部表情被广泛应用于情绪的行为解释，认知科学和社会互动。在本文中，我们提出了一种新的方法，全自动人脸表情识别的人脸图像序列。随着面部表情随着时间的推移而演变，使用基于弹性束图匹配位移估计的位移，在连续视频帧中自动跟踪面部标志。提取来自各个地标的特征向量以及地标跟踪结果对，并相对于序列中的第一帧进行归一化。通过取训练面部表情序列的界标跟踪结果的中值，形成用于每类面部表情的原型表情序列。多类AdaBoost算法利用输入人脸表情特征向量与原型人脸表情之间的动态时间规整相似性距离，作为弱分类器来选择判别特征向量子集。最后，提出了两种人脸表情识别的方法，分别是使用多类AdaBoost和动态时间规整，或者使用支持向量机对提升后的特征向量进行识别。在Cohn-Kanade（CK+）人脸表情数据库上的实验结果表明，使用多类AdaBoost和支持向量机的识别准确率分别为95.17%和97.35%。
Facial expressions are widely used in the behavioral interpretation of emotions, cognitive science, and social interactions. In this paper, we present a novel method for fully automatic facial expression recognition in facial image sequences. As the facial expression evolves over time facial landmarks are automatically tracked in consecutive video frames, using displacements based on elastic bunch graph matching displacement estimation. Feature vectors from individual landmarks, as well as pairs of landmarks tracking results are extracted, and normalized, with respect to the first frame in the sequence. The prototypical expression sequence for each class of facial expression is formed, by taking the median of the landmark tracking results from the training facial expression sequences. Multi-class AdaBoost with dynamic time warping similarity distance between the feature vector of input facial expression and prototypical facial expression, is used as a weak classifier to select the subset of discriminative feature vectors. Finally, two methods for facial expression recognition are presented, either by using multi-class AdaBoost with dynamic time warping, or by using support vector machine on the boosted feature vectors. The results on the Cohn-Kanade (CK+) facial expression database show a recognition accuracy of 95.17% and 97.35% using multi-class AdaBoost and support vector machines, respectively.
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影响因子：
4.7
作者：
Sandbach, Georgia;Zafeiriou, Stefanos;Rueckert, Daniel
通讯作者：
Rueckert, Daniel
影响因子：
4.7
作者：
Sebe, N.;Lew, M. S.;Huang, T. S.
通讯作者：
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影响因子：
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作者：
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通讯作者：
Chae, Oksam
DOI：
10.1109/tassp.1978.1163055
发表时间：
1978-01-01
期刊：
IEEE TRANSACTIONS ON ACOUSTICS SPEECH AND SIGNAL PROCESSING
影响因子：
--
作者：
SAKOE, H;CHIBA, S
通讯作者：
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影响因子：
8
作者：
Lemire, Daniel
通讯作者：
Lemire, Daniel