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	Multiple Models Fusion for Emotion Recognition in the Wild
	Wu, Jianlong ; Lin, Zhouchen ; Zha, Hongbin
	2015
关键词	Emotion Recognition Multiple Models Fusion Bag of Features EmotiW 2015 Challenge FACIAL EXPRESSION RECOGNITION LOCAL BINARY PATTERNS IMAGE CLASSIFICATION FEATURES SCALE
英文摘要	Emotion recognition in the wild is a very challenging task. In this paper, we propose a multiple models fusion method to automatically recognize the expression in the video clip as part of the third Emotion Recognition in the Wild Challenge (EmotiW2015). In our method, we first extract dense SIFT, LBP-TOP and audio features from each video clip. For dense SIFT features, we use the bag of features (BoF) model with two different encoding methods (locality-constrained linear coding and group saliency based coding) to further represent it. During the classification process, we use partial least square regression to calculate the regression value of each model. By learning the optimal weight of each model based on the regression value, we fuse these models together. We conduct experiments on the given validation and test datasets, and achieve superior performance. The best recognition accuracy of our fusion method is 52:50% on the test dataset, which is 13:17% higher than the challenge baseline accuracy of 39:33%.; EI; CPCI-S(ISTP); jlwu1992@pku.edu.cn; zlin@pku.edu.cn; zha@cis.pku.edu.cn; 475-481
语种	英语
出处	2015 ACM International Conference on Multimodal Interaction
DOI标识	10.1145/2818346.2830582
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/436509]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Wu, Jianlong,Lin, Zhouchen,Zha, Hongbin. Multiple Models Fusion for Emotion Recognition in the Wild. 2015-01-01.

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