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From those comparison results, we can conclude some statements that:
-Most of the cases, deep learning-based methods are capable of learning facial characteristics and improving facial expression recognition.
-Traditional machine learning-based approaches can nicely solve the FER problem on JAFFE and CK+ which are small datasets captured in laboratory conditions. Here, both Haar and HoG are easy to use, fast to train and bought up good results - 98.90% (Haar features) and 99.79% (HoG features) corresponding on JAFFE and CK+.
-Evaluations in FER2013 demonstrate that deep learning-based methods are the best candidate to address the problem in real conditions with different illumination, pose and resolution.
-Data augmentation is applied in most deep learning methods training FER2013. The more data it has, the better performance it can obtain.
-Hybrid and ensemble learning has been attracting more and more researchers since they did help in improving the model accuracy. |
IEEE/ICACT20210175 Slide.32
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