Abstract
This paper focus on the issue of recognition of facial expressions in video sequences and propose a local-with-global method, which is based on local enhanced motion history image and CNN-RNN networks. On the one hand, traditional motion history image method is improved by using detected human facial landmarks as attention areas to boost local value in difference image calculation, so that the action of crucial facial unit can be captured effectively, then the generated LEMHI is fed into a CNN network for categorization. On the other hand, a CNN-LSTM model is used as an global feature extractor and classifier for video emotion recognition. Finally, a random search weighted summation strategy is selected as our late-fusion fashion to final predication. Experiments on AFEW, CK+ and MMI datasets using subject-independent validation scheme demonstrate that the integrated framework achieves a better performance than state-of-arts methods.
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Acknowledgments
This research has been partially supported by National Natural Science Foundation of China under Grant Nos. 61672202, 61502141 and 61432004.
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Wang, H., Zhou, G., Hu, M., Wang, X. (2018). Video Emotion Recognition Using Local Enhanced Motion History Image and CNN-RNN Networks. In: Zhou, J., et al. Biometric Recognition. CCBR 2018. Lecture Notes in Computer Science(), vol 10996. Springer, Cham. https://doi.org/10.1007/978-3-319-97909-0_12
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