Face matching consists of the ability to decide whether two face images (or more) belong to the same person or to different identities. Face matching is crucial for efficient face recognition and plays an important role in applied settings such as passport control and eyewitness memory. However, despite extensive research, the mechanisms that govern face-matching performance are still not well understood. Moreover, to date, many researchers hold on to the belief that match and mismatch conditions are governed by two separate systems, an assumption that likely thwarted the development of a unified model of face matching. The present study outlines a unified unequal variance confidence-similarity signal detection-based model of face-matching performance, one that facilitates the use of receiver operating characteristics (ROC) and confidence-accuracy plots to better understand the relations between match and mismatch conditions, and their relations to factors of confidence and similarity. A binomial feature-matching mechanism is developed to support this signal detection model. The model can account for the presence of both within-identities and between-identities sources of variation in face recognition and explains a myriad of face-matching phenomena, including the match-mismatch dissociation. The model is also capable of generating new predictions concerning the role of confidence and similarity and their intricate relations with accuracy. The new model was tested against six alternative competing models (some postulate discrete rather than continuous representations) in three experiments. Data analyses consisted of hierarchically nested model fitting, ROC curve analyses, and confidence-accuracy plots analyses. All of these provided substantial support in the signal detection-based confidence-similarity model. The model suggests that the accuracy of face-matching performance can be predicted by the degree of similarity/dissimilarity of the depicted faces and the level of confidence in the decision. Moreover, according to the model, confidence and similarity ratings are strongly correlated. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

中文翻译：

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基于信号检测的人脸匹配置信相似度模型。

人脸匹配包括确定两个人脸图像（或多张）是否属于同一个人或不同身份的能力。人脸匹配对于高效的人脸识别至关重要，并且在护照控制和目击者记忆等应用环境中发挥着重要作用。然而，尽管进行了广泛的研究，但控制面部匹配性能的机制仍然没有得到很好的理解。此外，迄今为止，许多研究人员坚持认为匹配和不匹配条件是由两个独立的系统控制的，这一假设可能会阻碍面部匹配统一模型的开发。本研究概述了一种基于统一不等方差置信相似度信号检测的人脸匹配性能模型，该模型有助于使用接收器操作特征（ROC）和置信精度图来更好地理解匹配和不匹配条件之间的关系，以及它们与置信度和相似性因素的关系。开发了二项式特征匹配机制来支持该信号检测模型。该模型可以解释人脸识别中身份内部和身份之间变异来源的存在，并解释无数的人脸匹配现象，包括匹配与不匹配分离。该模型还能够生成有关置信度和相似性的作用及其与准确性的复杂关系的新预测。在三个实验中，新模型与六种替代竞争模型（一些假设离散而不是连续表示）进行了测试。数据分析包括分层嵌套模型拟合、ROC 曲线分析和置信精度图分析。所有这些都为基于信号检测的置信相似度模型提供了实质性支持。该模型表明，面部匹配性能的准确性可以通过所描绘面部的相似/不相似程度以及决策的置信度来预测。此外，根据该模型，置信度和相似度评级密切相关。（PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。