# A CNN-Based Approach for Driver Drowsiness Detection by Real-Time Eye State Identification
## 2 Related Work  
In the research of [PERSON] et al. [17], an architecture called deep drowsiness detection (DDD) is proposed, which processes RGB videos that focus on the driver's entire face. The DDD architecture makes use of three architectures: AlexNet, VGG-FaceNet and FlowImageNet, where the output of the three networks are unified in order to classify the drowsiness in frames of the input videos. To test the proposed model, the authors use the NTHU drowsy driver detection (NTHU-DDD) database, achieving an average accuracy of 73.06% during their experimental results. [PERSON] et al. [18] proposed an architecture that specifically uses the eye region. For the extraction of the eye region, the Haar Cascade technique proposed by [PERSON] was used. To detect the face and eyes, the ROI of the eyes becomes the input of their CNN where they used a database collected for the training of their network, obtaining an accuracy of 98% in training, 97% in validation and 96.42% in the final test. In the approach of [PERSON] et al. [19], the authors used facial characteristic points for drowsiness detection and classification. They made use of an MTCNN (multi-task cascaded convolutional network) for face detection and characteristic point location, extracting ROIs from the eyes and mouth that pass to their network called EM-CNN, where they make a classification of four classes, two for the eyes state and two for the mouth state. Their tests were performed on a database provided by the company Biteda, where they obtained 93.623% accuracy compared to other types of architectures. In the proposal by [PERSON] et al. [20], two methods were proposed for drowsiness detection, the first one uses characteristic points of the face focusing on the eyes and mouth using the Dlib library, applying thresholds to determine if it is yawning or blinking. The second method uses MobileNet-V2 and ResNet-50V2 networks using transfer learning. For the training of CNNs, the authors collected images from various sources to generate their dataset, obtaining an average result of 97% accuracy. In the system presented by [PERSON] et al. [2018], Haar Cascade was used to extract the eyes. The ROI extraction was performed for each eye separately after detecting the face, then the proposed architecture was used for training, using two databases: YawDDD and Closed Eyes In The Wild. The authors achieved an accuracy of 96.82%. In the research presented by [PERSON] et al. [2018], a drowsiness detection system was proposed by training three CNNs, one designed by the authors and the others by transfer learning using VGG16 and VGG19. The face detection was performed by Haar Cascade, and then Dlib was used to detect the eye points and thus delimit the region of interest for the training of the three networks using the ZJU Eyeblink database. Their results showed an accuracy of 98.15% with the proposed network. In the proposal presented by [PERSON] and [PERSON] [2018], a 3D-CNN was implemented for the extraction of spatio-temporal features; these learned features were used as inputs for a long-term short-term memory (LSTM). The authors propose two 3D-CNN+LSTM models (A and B) tested on two datasets (3 MDAD and YawDDD), obtaining a 96% accuracy on YawDDD. [PERSON] et al. [2018] proposed several architectures combined with CNN and LSTM, the CNNs were based on ResNet, VGG-16, GoogleNet and MobileNet. The authors also propose their own architecture called \"CNN-LSTM\", training and testing the architectures on the \"NTHU\" dataset. Their network \"CNN-LSTM\" obtained an accuracy of 98.3% in training and 97.31% in testing, outperforming the other four networks. [PERSON] et al. [2019] presented a system to detect drowsiness based on eye aspect ratio (EAR) combined with PERCLOS, which calculates the percentage of eye closure for a period of time. They used Dlib for eye point extraction, which is necessary for EAR. Their system had an 80% accuracy for their presented method. Finally, in the research of [PERSON] et al. [2018], they proposed a CNN architecture.