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Hi, I'm a student in the Artificial Intelligence course. For my final year project, I would like to propose a driver drowsiness detection system using Haar Cascade and Eye Aspect Ratio on Raspberry Pi 4. I believe that using Haar Cascade for face detection is suitable for Raspberry Pi 4 since it won't have a GPU and Haar Cascade is the fastest method for face detection. I'm aware of the cons of using the Haar Cascade such as not very good accuracy and so on. The question that I want to ask is, do I need to use any dataset for this project? I believe that OpenCV have a repository for fully trained models (https://github.com/opencv/opencv/tree/master/data/haarcascades) and I can just download the model from there. But I think my final year project requires me to have a dataset. Since I'm using the Eye Aspect Ratio to conclude the driver drowsiness, I think I don't need a dataset for the project because I can just use the Haar Cascade to detect face and then use the EAR to conclude the driver's drowsiness. Also, can I tell my lecturer that people normally train data (using Cascade-Trainer-GUI) in Haar Cascade only for object detection, not face detection? Thanks in advance!