Convolutional Neural Networks For Eye Detection Trained With Manually And Automatically Generated Ground Truth Data
Sorin Valcan, Mihail Găianu · 2023
Eye detection represents an essential facial feature to be detected in driver monitoring systems representing the basis for further processing for attention or drowsiness detection. The machine learning approach for infrared image vision problems comes with multiple obstacles like creating a recordings data set with drivers and labeling it to be able to train a model. In very strict areas like automotive or medical imaging machine learning approaches are still a big debate especially because of the black box that is represented by the model, meaning that a wrong detection is impossible to be predicted or explained. That’s why the entire focus is shifted to control the data set and the labeling process with very hard manual effort. This paper presents the experimental results of training convolutional neural networks for eye detection in automotive industry using ground truth data obtained from an automatic labeling module. These results are compared with neural networks that were trained using the same data set but with labels created by manual human effort. This experiment shows that similar accuracy of convolutional neural network can be obtained in image vision problems without manual work for image labeling.