Combine Feature Selection with Timing Sequence Energy Analysis for Driving Drowsiness Detection

In this work, we try another way by introducing a novel method which combines feature selection with time sequence analysis techniques to estimate driving drowsiness. Kernelized fuzzy rough sets based technique is used to evaluate quality of candidate features and select the most useful one. S transform is adopted for blink energy analysis. Finally the experiments on three blink sequences with dissimilar fatigue degree are used to validate our ideas.