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首页> 外文会议>IAPR TC3 Workshop on pattern recognition of social signals in human-computer-interaction >Active Shape Model vs. Deep Learning for Facial Emotion Recognition in Security
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Active Shape Model vs. Deep Learning for Facial Emotion Recognition in Security

机译：主动形状模型与深度学习在安全性中的面部表情识别

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摘要

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%.
机译：随着面部情感识别每天变得越来越重要，进行了一项研究实验以找到面部情感识别的最佳方法。测试了深度学习（DL）和主动形状模型（ASM）。过去，研究人员通过深度学习和主动形状模型研究了面部表情识别技术，希望找出哪种方法更适合此类技术。两种方法都用两个不同的数据集进行了测试，我们的发现是一致的。经测试，主动形状模型比深度学习更好。但是，深度学习更快，更容易实现，这意味着，通过使用更好的深度学习软件，深度学习将在识别和分类面部表情方面更好。对于此实验，深度学习显示CAFE数据集的准确性为60％，而Active Shape Model显示的准确性为93％。 JAFFE数据集也是如此;深度学习的准确性为63％，活动形状模型的准确性为83％。

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