ScaleFace: Uncertainty-aware Deep Metric Learning
This addresses the challenge of uncertainty estimation in complex scenarios like face recognition for improved reliability, though it is incremental as it builds on existing uncertainty-aware methods.
The paper tackles the problem of predicting how input quality affects accuracy in deep metric learning by proposing ScaleFace, which estimates uncertainty with minimal extra cost, achieving superior performance in face recognition and text-to-image retrieval tasks.
The performance of modern deep learning-based systems dramatically depends on the quality of input objects. For example, face recognition quality would be lower for blurry or corrupted inputs. However, it is hard to predict the influence of input quality on the resulting accuracy in more complex scenarios. We propose an approach for deep metric learning that allows direct estimation of the uncertainty with almost no additional computational cost. The developed \textit{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, thus allowing uncertainty estimation. We provide comprehensive experiments on face recognition tasks that show the superior performance of ScaleFace compared to other uncertainty-aware face recognition approaches. We also extend the results to the task of text-to-image retrieval showing that the proposed approach beats the competitors with significant margin.