Overcoming occlusions in the wild: A multi-task age head approach to age estimation

• Proposed a novel framework for occluded facial age estimation. • ARCM enhances feature representation via attention, convolution, and residual layers. • MTAH combines regression and distribution learning to boost age estimation accuracy. • The proposed approach outperformed SOTA on three benchmark age estimation datasets. Facial age estimation has achieved considerable success under controlled conditions. However, in unconstrained real-world scenarios, which are often referred to as ”in the wild”, age estimation remains challenging, especially when faces are partially occluded, which may obscure their visibility. To address this limitation, we propose a new approach integrating generative adversarial networks (GANs) and transformer architectures to enable robust age estimation from occluded faces. We employ an SN-Patch GAN to effectively remove occlusions, while an Attentive Residual Convolution Module (ARCM), paired with a Swin Transformer, enhances feature representation. Additionally, we introduce a Multi-Task Age Head (MTAH) that combines regression and distribution learning, further improving age estimation under occlusion. Experimental results on the FG-NET, UTKFace, and MORPH datasets demonstrate that our proposed approach surpasses existing state-of-the-art techniques for occluded facial age estimation by achieving an MAE of 3.00, 4.54, and 2.53 years, respectively.