Facial Feature Recognition with Multi-Task Learning and Attention-based Enhancements

Abstract

Facial feature recognition (FFR) has witnessed a remarkable surge in recent years, driven by its extensive applications in identity recognition, security, and intelligent imaging. The UTKFace dataset plays a pivotal role in advancing FFR by providing a rich dataset of facial images with accurate age, gender, and race labels. This paper proposes a novel multi-task learning (MTL) model that leverages the powerful Efficient-Net architecture and incorporates attention-based learning with two key innovations. First, we introduce an age-specific loss function that minimizes the impact of errors in less critical cases while focusing the learning process on accurate age estimation within sensitive age ranges. This innovation is trained using the UTKFace dataset and is specifically optimized to improve accuracy in age estimation across different age groups. Second, we present an enhanced attention mechanism that guides the model to prioritize features that contribute to more robust FFR. This mechanism is trained on the diverse and challenging images of UTKFace and is capable of identifying subtle and discriminative features in faces for more accurate gender, race, and age recognition. Furthermore, our proposed method achieves a 30% reduction in model parameters compared to the baseline network while maintaining accuracy. Extensive comparisons with existing state-of-the-art methods demonstrate the efficiency and effectiveness of our proposed approach. Using the UTKFace dataset as the evaluation benchmark, our model achieves a 0.62% improvement in gender recognition accuracy, a 2.35% improvement in race recognition accuracy, and a noteworthy 3.23-year reduction in mean absolute error for age estimation.