Applications of Deep Learning in Student Concentration Analysis
With the ever-improving of the technology, students usually use the laptop and cellphone to take notes or look up information. However, in this situation, students with poor self-control are easily distracted by electronic products. Also, students with poor concentration can be absent-minded and wandering. Therefore, the purpose of this paper is to analyze students’ concentration through color images and help the teacher understand students’ situations. In this paper, we use the face detector and neural network to find facial landmarks in order to determine the facial information and estimate the face orientation. In addition, we also use the neural network and object recognition to classify these skeleton features extracted from the skeleton data of the pose estimation system. Finally, we analyze the students’ concentration according to the above results. In this paper, two kinds of fatigue behaviors are designed on the facial information and the occurrence of fatigue behaviors is determined by facial landmarks. Also, we use the features extracted from facial landmarks to train the neural network to estimate the face orientation angles with an average angular error of less than 10 degrees. In motion recognition, we design eight kinds of common postures of students, four of which are human-object interaction, and to collect the dataset to train and test the neural network. In the experiments, the recognition rate of person and angle independent experiments is nearly 80%. Also, the recognition rate of the real situation is good. With these experiments, it is proved that the system can provide accurate information in facial information and motion recognition.
Keywords: students’ attention, computer vision, activity recognition, neural networks