ScaleFace: Uncertainty-aware Deep Metric Learning

Roman Kail, Kirill Fedyanin, Nikita Muravev, Alexey Zaytsev, Maxim Panov

Abstract: The performance of modern deep learning-based systems dramatically depends on the quality of input objects. For example, face recognition quality is lower for blurry or corrupted inputs. Moreover, it is difficult to predict the influence of input quality on the resulting accuracy in more complex scenarios. We propose a deep metric learning framework that allows for direct estimation of the uncertainty with almost no additional computational cost. The developed ScaleFace algorithm uses trainable scale values that modify similarities in the space of embeddings. These input-dependent scale values represent a measure of confidence in the recognition result, thereby providing provably reasonable uncertainty estimation. We present results from comprehensive experiments on open-set classification tasks, including face recognition, which demonstrate the superior performance of ScaleFace compared to other uncertainty-aware face recognition approaches. We also extend our study to the task of text-to-image retrieval, showing that the proposed approach outperforms competitors by significant margins.