Towards In-Vehicle Multi-Task Facial Attribute Recognition: Investigating Synthetic Data and Vision Foundation Models

arXiv:2403.06088v1 Announce Type: cross
Abstract: In the burgeoning field of intelligent transportation systems, enhancing vehicle-driver interaction through facial attribute recognition, such as facial expression, eye gaze, age, etc., is of paramount importance for safety, personalization, and overall user experience. However, the scarcity of comprehensive large-scale, real-world datasets poses a significant challenge for training robust multi-task models. Existing literature often overlooks the potential of synthetic datasets and the comparative efficacy of state-of-the-art vision foundation models in such constrained settings. This paper …

abstract age arxiv cs.ai cs.cv cs.lg data datasets driver eess.iv etc experience foundation however importance intelligent intelligent transportation personalization recognition safety scale synthetic synthetic data systems through transportation type vision world