基于多特征量贝叶斯融合的驾驶疲劳识别  

Recognition of driver fatigue using multi-feature fusion by Bayesian network

作　　者：张伟[1] 黄炜[2] 罗大庸[1] 

机构地区：[1]中南大学信息科学与工程学院,长沙410075 [2]长沙航空职业技术学院计算机系,长沙410014

出　　处：《计算机工程与应用》2012年第33期244-248,共5页Computer Engineering and Applications

基　　金：国家自然科学基金(No.50808025);国家博士点基金资助项目(No.20090162110057);湖南省自然科学基金(No.05JJ30121)

摘　　要：针对保局投影的局限提出了正交流形保持投影方法,通过在LPP目标函数中引入非临近约束,保持了样本在低维空间中的局部和全局结构,采用正交化过程重新求解了投影矩阵,使得投影后的特征维数进一步降低,提高了通过表情进行驾驶疲劳识别的准确性;为了进一步降低识别的误警率,通过贝叶斯网络实现了基于疲劳表情、哈欠频率、眼睛闭合度等特征融合的疲劳检测,通过实验验证了以上过程的优越性。In order to surmount the limitations of Locality Preserving Projections method（LPP）,Orthogonal Manifold Preserving Projections（OMPP）method is proposed,which preserves the local and global structure invariance of the samples in the low-dimensional space by integrated non-adjacent constraint information on the objective function of LPP.The orthogonalization process is applied to solving the projection matrix for further reducing the characteristics dimensions after projections,and all of these improve the accuracy of driver fatigue recognition by the expression.In order to further reduce the false alarm rate of recognition,the Bayesian net is applied to detecting the fatigue states by the fusion of multiple features such as fatigue expression,frequency of yawn,and the degree of eye closure.The superiority of the above process has been verified by experiments.

关 键 词：驾驶疲劳识别 疲劳表情 正交流形保持投影 信息融合 贝叶斯网络 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]