Drowsiness detection is vital in preventing traffic accidents. In this project, we propose three new algorithms for pupil and iris detection, lips localization and eyes state analysis, which we incorporate into a four step system for drowsiness detection: face detection, drowsiness parameters extraction from eyes, drowsiness parameter extraction from mouth and drowsiness level determination. Many current efforts, which are based on face analysis, focus only on using a single visual cue to characterize driver's state of alertness. This approach that relies on a single visual cue may encounter difficulty when the required visual features cannot be acquired accurately or reliably. There are few systems that use several visual cues to characterize driver's state of alertness. These systems are based on IR illuminators or training data. IR illuminators can be hazardous to eye health. Thus, our proposed system determines drowsiness level using a combination of several visual cues and contextual information. Also, it requires no training data at any step or IR illuminators. We analyzed and compared different parts of the systems with other methods using IMM, HCE, CVL database and 30 video sequences in two drowsy and active states from 15 persons. Finally, we achieved excellent drowsiness level results from the study population. We determined drowsiness level as follows: 1. The eyes and mouth state (detecting whether they were open or closed) was analyzed as 94.3% and 95.1 %, respectively; 2. Drowsiness level was determined in different situations such as normal blinking, fast blinking, normal speaking, yawning and long eye closure and 3. The participants were given a warning message when the drowsiness level reached over the threshold of 0.95.