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In recent times, driver drowsiness is one of the major reasons for road accidents that leads to severe physical injuries, deaths and significant economic losses. Hence, the existing driver drowsiness detection systems require a countermeasure device for the prevention of sleepiness related accident. This research paper aims to perform drowsiness detection with the help of driver's eye state, head pose, and mouth state information. Initially, the input data were collected from the public drowsy driver database. Then, the Camera Response Model (CRM) was applied to improve the quality of collected data. Also, viola-jones, and Kanade-Lucas-Tomasi (KLT) approaches were used to detect and track the driver's face, eye, and mouth regions from the input video. In this research study, Online Region-Based Active Contour Model (ORACM) algorithm was used to segment the driver's mouth region in order to obtain the threshold value. Successively, feature extraction; Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) was applied to extract the features from the detected eye region. The extracted features of the eye region were combined with the threshold value of mouth region and head pose angle. After extracting the feature vectors, infinite approach was utilized to choose the relevant feature vectors. Finally, the selected features were classified using Support Vector Machine (SVM) for classifying the stages of drowsiness detection. Simulation outcome illustrated that the proposed system increased the classification accuracy up to 5.52% as related to hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). [ABSTRACT FROM AUTHOR]