Applicability Evaluation of Synthetic Images for Performance Testing via Quality and Similarity Assessment

With the growing prevalence of biometric recognition technology, the demand for diverse large-scale test datasets is significant, and data collection costs are increasing. Consequently, synthetic images attract attention as a potential solution. However, some synthetic images deviate from target representativeness; even if visually close, subtle alterations can negatively impact biometric recognition accuracy. To address the challenge, this paper introduces a novel Applicability Evaluation framework for synthetic images and biometric engines based on the image quality and score similarity. Synthetic Image Quality Assessment (SIQA) evaluates how closely synthetic images approximate target representativeness. Score Similarity Assessment (SSA) compares biometric engine scores and verifies identity similarity that is crucial for performance testing. Experimental results demonstrate that this framework effectively identified combinations of synthetic images and biometric engines that maintain recognition accuracy while ensuring the reliability of biometric performance testing. It can improve the trustworthiness and efficiency of biometric performance testing.