Spontaneous Smile Recognition for Interest Detection
Interestis a critical bridge between cognitive and effective issues in learning.Students interest has great impact on learning performance.Hence,its necessary to detect students interest and make them more engaged in the learning process for productive learning.Students interest can be detected based on the facial expression recognition,e.g.,smile recognition.However,various head poses,different illumination,occlusion and low image resolution make smile recognition difficult.In this paper,a conditional random forest based approach is proposed to recognize spontaneous smile in natural environment.First,image patches are extracted within the eye and mouth regions instead of the whole face to improve the robustness and efficiency.Then,the conditional random forests based approach is presented to learn the relations between image patches and the smile/non-smile features conditional to head poses.Furthermore,a K-means based voting method is introduced to improve the discrimination capability of the approach.Experiments have been carried out with different spontaneous facial expression databases.The encouraging results suggest a strong potential for interest detection in natural environment.
Smile recognition Conditional random forest Interest detection Head poses
Zhenzhen Luo Leyuan Liu Jingying Chen Yuanyuan Liu Zhiming Su
National Engineering Research Center for E-Learning,Central China Normal University,Wuhan 430079,China
国际会议
第七届全国模式识别学术会议(The 7th Chinese Conference on Pattern Recognition,CCPR2016)
成都
英文
119-130
2016-11-03（万方平台首次上网日期，不代表论文的发表时间）