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	Group Emotion Recognition with Individual Facial Emotion CNNs and Global Image Based CNNs
	Lianzhi Tan; Kaipeng Zhang; Kai Wang; Xiaoxing Zeng; Xiaojiang Peng; Yu Qiao
	2017
会议地点	中国澳门
英文摘要	This paper presents our approach for group-level emotion recognition in the Emotion Recognition in the Wild Challenge 2017. The task is to classify an image into one of the group emotion such as positive, neutral or negative. Our approach is based on two types of Convolutional Neural Networks (CNNs), namely individual facial emotion CNNs and global image based CNNs. For the individual facial emotion CNNs, we first extract all the faces in an image, and assign the image label to all faces for training. In particular, we utilize a large-margin softmax loss for discriminative learning and we train two CNNs on both aligned and non-aligned faces. For the global image based CNNs, we compare several recent state-of-theart network structures and data augmentation strategies to boost performance. For a test image, we average the scores from all faces and the image to predict the final group emotion category. We win the challenge with accuracies 83.9% and 80.9% on the validation set and testing set respectively, which improve the baseline results by about 30%.
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/11764]
专题	深圳先进技术研究院_集成所
作者单位	2017
推荐引用方式 GB/T 7714	Lianzhi Tan,Kaipeng Zhang,Kai Wang,et al. Group Emotion Recognition with Individual Facial Emotion CNNs and Global Image Based CNNs[C]. 见:. 中国澳门.

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