Facial Expression Recognition Based on Multi-scale CNNs
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This paper proposes a new method for facial expression recognition, called multi-scale CNNs. It consists several sub-CNNs with different scales of input images. The sub-CNNs of multi-scale CNNs are benefited from various scaled input images to learn the optimalized parameters. After trained all these sub-CNNs separately, we can predict the facial expression of an image by extracting its features from the last fully connected layer of sub-CNNs in different scales and mapping the averaged features to the final classification probability. Multi-scale CNNs can classify facial expression more accurately than any single scale sub-CNN. On Facial Expression Recognition 2013 database, multi-scale CNNs achieved an accuracy of 71.80 % on the testing set, which is comparative to other state-of-the-art methods.
KeywordsFacial expression recognition Multi-scale CNNs CNN Deep learning Patten recognition
This work was supported by the National Key Research and Development Plan (Grant No.2016YFC0801002), the Chinese National Natural Science Foundation Projects \(\sharp \)61473291, \(\sharp \)61572501, \(\sharp \)61502491, \(\sharp \)61572536, Science and Technology Development Fund of Macau (No. 019/2014/A1), NVIDIA GPU donation program and AuthenMetric R&D Funds.
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