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Facial Emotion Recognition Based on Viola-Jones Algorithm in the Learning Environment

机译：基于Viola-Jones算法在学习环境中的面部情感识别

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An emotion is a trigger of learning success, so the learning should be adapting to the students' emotions. The most of popular approach is the acquisition of facial-based features. Therefore, we present facial emotion recognition based on the Viola-Jones Algorithm in the learning environment. Basically, the Viola-Jones algorithm is a face detection algorithm. However, we use facial-based features to detect face and recognize emotion, thus we applied rectangular feature and cascading AdaBoost algorithm which are the main concept of the Viola-Jones Algorithm in those both of process. In this study, we compare accuracy, precision, recall, and time-consuming of the Viola-Jones algorithm and our previous methods [1] using 50 UM's learning images in student emotion recognition. The accuracy, precision, recall, and time-consuming of Viola-Jones algorithm reach 0.74, 0.73, 0.76 and 15 seconds per frame, whereas our previous methods [1] reach 0.46, 0.48, 0.52, and 42 seconds per frame. In emotional recognition, we can conclude that the viola jones algorithm is superior to our previous research.
机译：情绪是学习成功的触发，所以学习应该适应学生的情绪。大多数流行的方法是收购面部基础的功能。因此，我们基于学习环境中的Viola-Jones算法呈现面部情感识别。基本上，Viola-Jones算法是面部检测算法。但是，我们使用基于面部的特征来检测面部并识别情绪，从而应用矩形特征和级联Adaboost算法，这些算法是那些过程中的中提琴算法的主要概念。在这项研究中，我们比较精度，精确，召回和耗时的中提琴算法和我们之前的方法[1]在学生情感认可中使用50倍的学习图像进行了比较[1]。每帧达到0.74,0.73,0.76和15秒的准确性，精度，召回和耗时，而我们之前的方法[1]达到0.46,0.48,0.52和42秒。在情感上的认可中，我们可以得出结论，Viola Jones算法优于我们以前的研究。

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