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Drowsiness is responsible for a large number car crashes. Blinks analysis from electrooculogram (EOG) signal brings reliable information on drowsiness but EOG recording condition can be really disturbing for the driver. On the other hand, video approaches seem a lot more practical but the standard acquisition rate does not give the same accuracy than EOG for blinks analysis. So, a high frame rate camera seems a good compromise. The purpose of this paper is to study to what extent a high speed camera could replace the EOG for the extraction of blinks features in order to design a system to detect drowsiness. An original method to detect and characterize blinks from the video is presented. This method uses two energy signals extracted from the video analysis: one related to the contours of the eyes and the other one to the moving contours. A comparison between the different features extracted from the EOG and from the video is then performed. This study shows that duration, frequency, PERCLOS 80 and dynamic features extracted from the EOG and from the video signals are highly correlated. The frame rate influence on the accuracy of the different features extracted is also studied.