Age-Invariant Face Embedding using the Wasserstein Distance
This addresses the problem of age-invariant face recognition for applications like security and biometrics, representing a novel method for a known bottleneck.
The paper tackles face verification in datasets with significant age differences by proposing a novel approach using multitask learning and a Wasserstein distance discriminator to disentangle age and identity embeddings, resulting in improved performance over state-of-the-art methods in terms of face verification accuracy.
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.