Advanced Multi-Task Learning with Lightweight Networks and Multi-Head Attention for Efficient Facial Attribute Estimation

Abstract

The rapid advancement of computer vision algorithms demands efficient computational resource utilization for practical applications. This study proposes a novel framework that integrates multi-task learning (MTL) with MobileNetV3-Large networks and multi-head attention (MHA) mechanisms to simultaneously estimate facial attributes, including age, gender, race, and emotions. By employing MHA, the model enhances feature extraction and representation by focusing on multiple regions of the input image, thereby reducing computational complexity while significantly improving accuracy. The Receptive Field Enhanced Multi-Task Cascaded (RFEMTC) technique is utilized for effective preprocessing of the input data. Our methodology is rigorously evaluated on the UTKFace, FairFace, and RAF-DB datasets. We introduce a weighted loss function to balance task contributions, enhancing overall performance. Through refinement of the network architecture by analyzing branching points and optimizing the balance between shared and task-specific layers, our experimental results demonstrate significant improvements: a 7% reduction in parameters, a 3% increase in gender detection accuracy, a 5% improvement in race detection accuracy, and a 6% enhancement in emotion detection accuracy compared to single-task methods. Additionally, our proposed architecture reduces age estimation error by approximately one year on the UTKFace dataset and improves age estimation accuracy on the FairFace dataset by 5% compared to state-of-the-art approaches.