Stating Comparison Score Uncertainty and Verification Decision Confidence Towards Transparent Face Recognition
This work addresses the need for transparent verification decisions in critical face recognition applications, representing an incremental improvement.
The paper tackled the problem of assessing trustworthiness in face recognition verification decisions by propagating model uncertainties to scores and decisions, resulting in experimentally proven suitability on three models and two datasets.
Face Recognition (FR) is increasingly used in critical verification decisions and thus, there is a need for assessing the trustworthiness of such decisions. The confidence of a decision is often based on the overall performance of the model or on the image quality. We propose to propagate model uncertainties to scores and decisions in an effort to increase the transparency of verification decisions. This work presents two contributions. First, we propose an approach to estimate the uncertainty of face comparison scores. Second, we introduce a confidence measure of the system's decision to provide insights into the verification decision. The suitability of the comparison scores uncertainties and the verification decision confidences have been experimentally proven on three face recognition models on two datasets.