Abstract: The present invention relates to a method and a system for training a machine learning model for face recognition. The method comprising generating a training dataset; providing a teacher model that comprises a teacher backbone and a teacher head; iteratively training the teacher backbone and the teacher head using the training dataset; setting the machine learning model to include a lightweight backbone and a lightweight head; copying trained parameters of the trained teacher head to the lightweight head; and iteratively training the lightweight backbone using the training dataset.

Abstract: A method and system for training a machine learning model for point of gaze prediction simultaneously analyzes the image of the scene and the image of the driver's face using the machine learning model to generate a point of gaze of a driver in traffic environments. The machine learning model comprises a camera calibration module that can compute an embedding vector that represents the spatial configuration between the driver and the camera system. The camera calibration module improves the overall model's performance, which is jointly trained end to end.

Abstract: A driver monitor system includes an image data acquiring module configured to acquire a plurality of image data from a data collection module; a training module configured to train a plurality of teacher models to obtain a plurality of feature groups using the plurality of image data, and transfer a plurality of pieces of knowledge obtained from the plurality of feature groups to a plurality of student models, respectively; and at least one edge device comprising the plurality of student models configured to use a pipeline design pattern with multiple threads to make a warning.