# Driver Drowsiness Detection by Applying Deep Learning Techniques to Sequences of Images
## 2 Background and Related Work  
When measuring the drowsiness level of a driver, there are two different approaches according to the origin of the data used for this measuring. On the one hand, there are systems that monitor the vehicle state to assess the fatigue of the driver, while on the other hand, there are systems that use parameters obtained from the own driver. _(a) Systems focused on the vehicle_  
Among works that focus on the analysis of the vehicle state and its relation to fatigue, the most common measures that are studied are steering wheel behaviors or lane departures [11; 12; 13]. In [14], other parameters of the car are used, such as the vehicle position or the steering wheel angle, and they perform data fusion on multiple measures to achieve a more reliable system. However, even if the diminishing performance over skill-based tasks by the driver can actually be a consequence of drowsiness, it appears at a later stage and it cannot be used to detect the early symptoms of fatigue [15]. _(b) Systems focused on the driver_  
One of the most reliable ways of estimating fatigue is by using electroencephalograms (EEG) in combination with electrooculograms (EOG) [16], but in real driving environments, these kinds of systems are usually rejected by drivers. Their main drawback is that they require that the driver has attached electrodes around the eyes and over the head, which makes them intrusive systems that produce discomfort and rejection by drivers. Because of this limitation, the most used fatigue detection systems are those in which the driver's state is detected through a camera placed on the vehicle that takes images of the driver. In this work, we will focus on the detection of the early symptoms of drowsiness by using the driver's state. There are many works that follow this approach, which use numerous and varied parameters and techniques for their detection. For example, in [17], the landmarks of the driver's face (that is, a group of points that locate the most important elements of the face: eyes, eyebrows, nose, mouth, and facial shape) are obtained, and then, using these landmarks, some parameters, such as the percentage of eye closure (PERCLOS), are calculated. Afterwards, these features are introduced on a support vector machine (SVM) that classifies whether the driver is tired or not. In [18], a combination of depth videos and deep learning is used for fatigue detection. In particular, it uses two CNNs: a _spatial CNN_, which detects object's positions, and a _temporal CNN_, which looks for information between two neighboring frames. By using these two CNNs, the system is able to calculate motion vectors from one frame to another, which allows to detect yawns, even when the driver uses a hand to cover his or her mouth. Fuzzy logic [10] becomes a powerful tool when developing systems that help protect drivers: on the one hand, because it is easy and intuitive to create rules that are accurate and whose results are easily understood, and, on the other hand, due to the fast computing of these kind of systems, which allows using them in real time. Examples of these systems are not limited to fatigue detection: in [19], for example, a fuzzy-based alarm system is proposed that alerts the driver of dangerous situations. Among works that combine fuzzy logic and fatigue detection, in [20], a system is proposed that analyzes the mouth and eyes of the driver, measuring its openness to assess whether the driver is fatigued or not, and if the system detects drowsiness over several consecutive frames, it raises an alarm. In contrast, in [21], the authors use measures that represent the driver's behavior over a window of time, as the average PERCLOS, the driver's blinking rate or the head position, all of it measured over the last 60 s. After this, these parameters are introduced in a fuzzy inference system (FIS) formed by 32 different rules, and the drowsiness level of the driver is calculated.