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.

翻译：本文研究在图像中同一人物存在显著年龄差异的数据集上的人脸验证问题。这对当前的面部识别与验证技术构成了重大挑战。为解决该问题，我们提出了一种新颖方法，利用多任务学习与Wasserstein距离判别器，将面部图像的年龄特征与身份特征进行解耦。该方法通过最小化Jensen-Shannon散度，借助Wasserstein距离判别器实现多任务学习，从而降低年龄嵌入与身份嵌入之间的互信息。这改进了面部图像中年龄与身份信息的编码能力，并提升了年龄变化数据集上的人脸验证性能。我们使用多个年龄变化面部数据集评估了该方法的效果，并证明其在人脸验证准确率上优于当前最先进的模型。