Evaluating Age Estimation Robustness Under Realistic Facial Occlusions

Facial age estimation has shown notable progress under controlled conditions. However, in unconstrained real-world environments, accurate age estimation remains challenging. This difficulty becomes more severe when facial images contain partial occlusions, as these obstructions hide important age-related information. Moreover, there is no publicly available occluded age estimation dataset to improve performance in real-world scenarios. To overcome this issue, we propose and publicly release three new datasets, FG-NET-O8, APPA-REAL-O8, and MORPH-O8, derived from existing benchmarks. These datasets contain eight types of realistic occlusions, providing a comprehensive testbed for age estimation under occlusions. These occlusions are generated using multiple diffusion-based methods, including Stable Diffusion Realistic Vision, Blended Latent Diffusion, and Fooocus, while preserving the facial identity of each subject. We also design and conduct a human survey to evaluate the quality of the generated occlusions. Furthermore, we test five state-of-the-art age estimation approaches to analyze the impact of real-world occlusions on age estimation performance. Experimental results demonstrate that all approaches exhibit severe performance degradation for nearly all occlusion types across all three datasets.