Fatigue and drowsiness detection techniques based on the external features
are under progress, and the methods of facial feature extraction require
further development. This paper discusses the innovative processes, efficient
methods, and recent advancements in the field of drowsiness and fatigue
detection. In this proposed model, a wide application is planned in the field
of artificial intelligence by defining the fundamentals of human-computer
interaction, facial expression recognition and driver fatigue-sleepiness
determination. This research outlines an efficient and effective three-phase
strategy for detecting drowsiness. Viola Jones is used to detect facial traits in
these three phases. Detection of yawning and tracking once the face has been
identified, the segmenting the skin, the system becomes lighting invariant
portion by itself, focusing on the chromatic components based on skin, and
to reject most of non-face image backdrops. The color eye tracking and
yawning detection are carried out by template matching with the correlation
coefficient. The vectors of features based on each of the above phases is
concatenated, and a binary result is obtained. The analysis of sound and
successive frames into fatigue and non-fatigue states has been classified. If
the time in fatigue state exceeds the threshold, the system will sound an
alarm. 

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