# Driver Drowsiness Detection by Applying Deep Learning Techniques to Sequences of Images
## 5 Discussion
### Comparison with Systems of the State of the Art
The authors mention that they use 70% of the data for training and 30% for testing, but there are no details on how the separation is done, so it is possible that it presents the same problem explained in the previous case. In [23], they use the UTA-RLDD dataset to train and test a CNN inspired by LeNet architecture [39] to detect fatigue in drivers. They also use the \"awake\" and \"drowsy\" videos, and their methodology is to classify a randomly generated frame of each video. The accuracy of the model is assessed using stratified five-fold cross validation and, according to the authors, in each fold, there are about 152 sleepy samples and about 151 vigilant samples. However, since there is no indication about if the training and test sets share frames from the same video, it is possible that it presents the same problems as [36; 38]. This way, it is reported an accuracy of 91.8% when classifying a single frame, much higher than the accuracy obtained by our systems when raising alarms on the full videos. In that same work, they also use an implementation of [43], which uses multiple CNNs to classify fatigue, over UTA-RLDD. In this case, the accuracy reaches 63%, which is more similar to the results reported by our systems. In [37], the authors use the FaceNet CNN [44] to extract facial features from the drivers' images, and then use either a multiclass SVM or a K-NN to classify those features into one of the three categories of the UTA-RLDD dataset. The accuracy obtained by both systems is high: the multiclass SVM reaches a 89% accuracy when classifying individual frames, while the K-NN outperforms it with a 94% accuracy. They use a total of 3000 images to train the models and another 300 images to validate them, but it is unknown to what videos these images belong or how they were selected. In [40], the authors combine convolutional neural networks and long short-term memory for fatigue detection. The proposed hybrid architecture is trained and evaluated on a relabeled dataset that combines multiple datasets, including UTA-RLDD, and classifies their videos into three classes: \"alert\", \"low vigilant\", and \"fatigue\". The relabeling process is performed at two levels: at the frame level (each frame is classified as one of the three categories) and at the minute level (each minute is classified). The accuracy obtained by this architecture is 54.71% on frame segment cases, and 43.05% on minute segment cases. According to the authors, unlike other databases, UTA-RLDD contains subtle facial features that are difficult to capture through the training process. This could explain the low accuracy values obtained. In [41], authors compare a CNN and a LSTM that evaluate drowsiness driver detection on UTA-RLDD dataset. Dlib's pretrained face detector is used for detecting landmarks of eyes and mouth and extracting the eye aspect ratio (EAR), mouth aspect ratio (MAR),mouth over eye aspect ratio (MOEAR) and pupil circularity (PC). After extracting these four features, the data of all participants are merged and a new dataset is created. In total, 75% of the data are used to train the models, and the remaining 25% are used to test these models. The overall accuracy obtained by the LSTM network is 0.64, and that obtained by CNN is 0.72. As in [36], the system accuracy may be affected by the fact that images from the same video are used in both the training and test sets. In addition, the false positive rate of both systems is around 22%, which makes them unsuitable for implementation in an ADAS. In [42], the authors use a LSTM that receives facial features (changes in eyes and mouth movements) as input data to predict fatigue. In this work, the dataset used to model the system include frames from alert and drowsy videos of only 16 participants. In this case, the training of the model is performed by applying a cross-validation process that uses 74% of the data. Once the cross validation is completed, the best performing model is chosen as the final model.