Age-Invariant Face Embedding using the Wasserstein Distance
- URL: http://arxiv.org/abs/2305.02745v1
- Date: Thu, 4 May 2023 11:33:37 GMT
- Title: Age-Invariant Face Embedding using the Wasserstein Distance
- Authors: Eran Dahan and Yosi Keller
- Abstract summary: We study face verification in datasets where images of the same individuals exhibit significant age differences.
We propose a novel approach that utilizes multitask learning and a Wasserstein distance discriminator to disentangle age and identity embeddings.
- Score: 10.508187462682308
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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