Abstract

One of the global main goal of the safety driving system is protecting the driver, passenger(s), car, and surrounding environment against accident which are caused by external and internal factors. Driver fatigue, one of the major internal factors, is a leading reason of vehicle breakdown according to a survey done by National Highway Traffic Safety Administration (NHTSA). Thus, it is necessary to build driver fatigue monitoring system. We, then, propose a technique based on optical imaging through digital camera that installed on the car dashboard. The camera detects and tracks the driver face. From the driver face, we can apply non-contact photoplesthymography (PPG) in order to get multiple physiological signals such as brainwave, cardiac and respiration pulses. Those physiological signals can be utilized to measure fatigue level. Alteration in facial features like eyes, mouth, and head, can be used to observe the driver fatigue as well. We propose to use supervised descent method (SDM) with scale-invariant feature transform (SIFT) to excerpt information from the facial features. To classify the fatigue level from those multiple parameters, support vector machine (SVM) will be implemented.

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