Abstract
As Facial Emotion Recognition is becoming more important everyday, A research experiment was conducted to find the best approach for Facial Emotion Recognition. Deep Learning (DL) and Active Shape Model (ASM) were tested. Researchers have worked with Facial Emotion Recognition in the past, with both Deep learning and Active Shape Model, with wanting to find out which approach is better for this kind of technology. Both methods were tested with two different datasets and our findings were consistent. Active shape Model was better when tested versus Deep Learning. However, Deep Learning was faster, and easier to implement, which means with better Deep Learning software, Deep Learning will be better in recognizing and classifying facial emotions. For this experiment Deep Learning showed accuracy for the CAFE dataset by 60% whereas Active Shape Model showed accuracy at 93%. Likewise with the JAFFE dataset; Deep Learning showed accuracy at 63% and Active Shape Model showed accuracy at 83%.
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Acknowledgment
This project is supported by the National Science Foundation under the award CNS1359323 This project is funded by the National Science Foundation for Undergraduate students. (https://sites.google.com/a/ualr.edu/cs-reu-site-ualr/).
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Bebawy, M., Anwar, S., Milanova, M. (2017). Active Shape Model vs. Deep Learning for Facial Emotion Recognition in Security. In: Schwenker, F., Scherer, S. (eds) Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction. MPRSS 2016. Lecture Notes in Computer Science(), vol 10183. Springer, Cham. https://doi.org/10.1007/978-3-319-59259-6_1
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