Multi-task Learning of Facial Landmarks and Expression
Computer and Robot Vision（2014）
摘要
Recently, deep neural networks have been shown to perform competitively on the task of predicting facial expression from images. Trained by gradient-based methods, these networks are amenable to \"multi-task\" learning via a multiple term objective. In this paper we demonstrate that learning representations to predict the position and shape of facial landmarks can improve expression recognition from images. We show competitive results on two large-scale datasets, the ICML 2013 Facial Expression Recognition challenge, and the Toronto Face Database.
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关键词
face recognition,image representation,learning (artificial intelligence),neural nets,ICML 2013 facial expression recognition challenge,Toronto Face Database,deep neural networks,facial expression,facial landmarks,gradient-based methods,image recognition,learning representations,multiple term objective,multitask learning,computer vision,convolutional neural networks,deep learning,emotion recognition,expression recognition,facial landmarks,multitask learning,representation learning
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