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The detection of person fatigue is one of the important tasks to detect drowsiness in the domain of image processing. Though lots of work has been carried out in this regard, there is a void of work shows the exact correctness. In this chapter, the main objective is to present an efficient approach that is a combination of both eye state detection and yawn in unconstrained environments. In the first proposed method, the face region and then eyes and mouth are detected. Histograms of Oriented Gradients (HOG) features are extracted from detected eyes. These features are fed to Support Vector Machine (SVM) classifier that classifies the eye state as closed or not closed. Distance between intensity changes in the mouth map is used to detect yawn. In second proposed method, off-the-shelf face detectors and facial landmark detectors are used to detect the features, and a novel eye and mouth metric is proposed. The eye results obtained are checked for consistency with yawn detection results in both the proposed methods. If any one of the results is indicating fatigue, the result is considered as fatigue. Second proposed method outperforms first method on two standard data sets.