Diffusion probabilistic models (DPMs) have exhibited exceptional proficiency
in generating visual media of outstanding quality and realism. Nonetheless,
their potential in non-generative domains, such as face recognition, has yet to
be thoroughly investigated. Meanwhile, despite the extensive development of
multi-modal face recognition methods, their emphasis has predominantly centered
on visual modalities. In this context, face recognition through textual
description presents a unique and promising solution that not only transcends
the limitations from application scenarios but also expands the potential for
research in the field of cross-modal face recognition. It is regrettable that
this avenue remains unexplored and underutilized, a consequence from the
challenges mainly associated with three aspects: 1) the intrinsic imprecision
of verbal descriptions; 2) the significant gaps between texts and images; and
3) the immense hurdle posed by insufficient databases.To tackle this problem,
we present DiFace, a solution that effectively achieves face recognition via
text through a controllable diffusion process, by establishing its theoretical
connection with probability transport. Our approach not only unleashes the
potential of DPMs across a broader spectrum of tasks but also achieves, to the
best of our knowledge, a significant accuracy in text-to-image face recognition
for the first time, as demonstrated by our experiments on verification and
identification.

通过建立理论连接和概率传递，DiFace 是一种通过可控的扩散过程来实现面部识别的解决方案，不仅在更广泛的任务领域释放了 DPMs 的潜力，而且在文本到图像的面部识别中实现了显著的准确性，这是我们知道的首次，这一点通过我们在验证和识别实验中的结果得到证实。