In this paper, we discuss the problem of estimating the pose of an automobile driver from video of the driver as he or she drives the vehicle. The results reported are a follow-on to those presented in the IEEE Intelligent Transportation Systems Conference 2000 by the same authors. The previous results pertained to pose classification using a non-parametric eigenface approach. Although the eigenface approach yielded impressive results, there were certain types of mis-classification errors that could be eliminated perhaps by using a different approach. In this paper, classification results obtained by another non-parametric approach, namely Fisherfaces, are compared with the eigenface approach. These results show that Fisherfaces outperform eigenfaces.
Comparison between eigenfaces and Fisherfaces for estimating driver pose
2001-01-01
498929 byte
Conference paper
Electronic Resource
English
Comparison Between Eigenfaces and Fisherfaces for Estimating Driver Pose
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