Face pose estimation for driver distraction monitoring by automatic clustered linear discriminant analysis

Smooth varying data is hard to classify/divide to separate classes since there is small separation. Large number of close and adjacent poses create smooth varying manifolds. Manual class formation by selecting different data points from entire database into different training classes will affect the error rate in smooth varying data classification. This paper proposes classification of smooth varying data based on clustering and discriminant analysis. The clustering process results in different clusters which can be used for classification based on discriminant analysis. The automated class formation based on the data points in the manifold reduces effort of manual clustering and it gives very comparable results. This pose estimation can be used as a measure of driver distraction monitoring.