基于头部姿态的学习注意力判别研究

学习注意力是学生学习效率的一个重要因素,直接影响学生的学习效果。为了有效监测学生在传统课堂教学中的注意力情况,提出一种基于头部姿态识别的学生注意力判别方法。首先运用卷积神经网络对视频图像的面部特征点进行检测,其次采用比例正交投影迭代变换(pose from orthography and scaling with iterations,POSIT)算法对人脸进行跟踪识别,并对人脸的旋转角度进行计算,根据其头部的倾斜角度对学生注意力进行分析研究。测试结果表明,提出的模型注意力检测准确率为88.7%,可以有效地对学生注意力进行检测,具有较好的应用前景。

A head posture based learning attention assessment algorithm

Abstract] Learning attention is an important factor of students'' learning efficiency, which directly affects students'' learning effect. A?method?of?student?attention?discrimination?is?proposed?based?on Head gesture?recognition?to?effectively?monitor?student?attention?in?traditional?classroom?teaching.Firstly,?a?convolution?neural?network?is?used?to?detect?facial?feature?points?on?video?images.?Secondly,the?POSIT?algorithm?is?adopted?to?track?and?recognize?human?faces.?At?the?same?time,?rotation?angles?of?human?faces?are?computed.?The?tilt?angles?of?heads?are?applied?to?study?students?attention.Experimental?results?show?that?attention?detection?accuracy?of?the?proposed?model?is?88.7%,?which?can?effectively?detect?students?attention?with?a?good?application?prospect.