  In this work, we study face verification in datasets where images of the same
individuals exhibit significant age differences. This poses a major challenge
for current face recognition and verification techniques. To address this
issue, we propose a novel approach that utilizes multitask learning and a
Wasserstein distance discriminator to disentangle age and identity embeddings
of facial images. Our approach employs multitask learning with a Wasserstein
distance discriminator that minimizes the mutual information between the age
and identity embeddings by minimizing the Jensen-Shannon divergence. This
improves the encoding of age and identity information in face images and
enhances the performance of face verification in age-variant datasets. We
evaluate the effectiveness of our approach using multiple age-variant face
datasets and demonstrate its superiority over state-of-the-art methods in terms
of face verification accuracy.
