Smile detection in the wild with deep convolutional neural networks

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• 作者：Junkai Chen ; Qihao Ou ; Zheru Chi ; Hong Fu
• 关键词：Smile detection ; In the wild ; Deep learning ; Feature learning ; Convolution neural network
• 刊名：Machine Vision and Applications
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：28
• 期：1-2
• 页码：173-183
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Pattern Recognition; Image Processing and Computer Vision; Communications Engineering, Networks;
• 出版者：Springer Berlin Heidelberg
• ISSN：1432-1769
• 卷排序：28

文摘

Smile or happiness is one of the most universal facial expressions in our daily life. Smile detection in the wild is an important and challenging problem, which has attracted a growing attention from affective computing community. In this paper, we present an efficient approach for smile detection in the wild with deep learning. Different from some previous work which extracted hand-crafted features from face images and trained a classifier to perform smile recognition in a two-step approach, deep learning can effectively combine feature learning and classification into a single model. In this study, we apply the deep convolutional network, a popular deep learning model, to handle this problem. We construct a deep convolutional network called Smile-CNN to perform feature learning and smile detection simultaneously. Experimental results demonstrate that although a deep learning model is generally developed for tackling “big data,” the model can also effectively deal with “small data.” We further investigate into the discriminative power of the learned features, which are taken from the neuron activations of the last hidden layer of our Smile-CNN. By using the learned features to train an SVM or AdaBoost classifier, we show that the learned features have impressive discriminative ability. Experiments conducted on the GENKI4K database demonstrate that our approach can achieve a promising performance in smile detection.