基于多角度面部特征的文献阅读专注度研究

Studying Literature Reading Concentration Based on Multi-angle Facial Features

【目的】文献阅读专注度目前大多采用人工方式或眼动跟踪方法进行评价,为实现专注度评价过程的自动化检测和实时反馈,本文将计算机视觉技术和专注度评价研究相结合,对智能技术在智慧知识服务中的应用研究也有意义。【方法】通过阅读者头部垂直方向和水平方向转动角度检测头部姿态;通过眼部以及嘴部的闭合度检测阅读者闭眼或打哈欠状态进而对疲劳度进行评分;并且依据阅读者的表情识别结果对情绪进行评分,之后应用模糊综合评价算法对相关因素进行权重确定和评分整合,获得阅读者在文献阅读过程中不同时刻的专注度状态。【结果】将该文献阅读专注度模型应用于实际阅读场景以评价头部倾斜、疲劳和消极情绪状态文献阅读专注度,获得的效果分别比正常状态低26.3%、25.2%和6.8%。【局限】当文献阅读视频出现面部特征模糊时,视觉识别技术检测精度不足,同时存在部分极端阅读实例有待优化。【结论】本文模型可以应用于多领域的下游任务中,既可以辅助阅读者及时调整文献阅读策略以提高阅读效率,也可以辅助图书馆等部门制定图书采购策略,进而减少图书资源浪费。

[Objective]The concentration of literature reading is mainly evaluated by manual methods or eyetracking techniques.This paper uses computer vision technology to automatically detect and receive real-time feedback from the concentration evaluation,which also improves the application of intelligent technology in smart knowledge service.[Methods]First,we detected the head postures of the readers by their vertical and horizontal rotation angles.Then,we scored their fatigue and emotion with the closing eyes or yawning status.Third,we decided the readers’sentiment based on these expression recognition results.Fourth,we applied the fuzzy comprehensive evaluation algorithm to determine the weight of relevant factors.Finally,we integrated the scores to obtain the reader’s concentration status at different reading processes.[Results]We applied the new model to the actual reading scenes to evaluate the reading concentration of head tilt,fatigue,and negative emotion,and the results were 26.3%,25.2%,and 6.8%lower than the normal state,respectively.[Limitations]When the literature reading video showed blurred facial features,the detection accuracy was unsatisfactory,which needs improvement.There are also some extreme reading instances to be optimized.[Conclusions]The proposed model can adjust reading strategies and help libraries optimize collection development strategies.