The ability to match faces correctly is crucial for efficient face recognition. Face-matchingalso plays an important role in applied setting such as passport control and eyewitnessmemory. However, despite extensive research on face-matching the mechanisms thatgovern this task are still not well understood. Moreover, to-date, many researchers holdon to the belief that match and mismatch responses are governed by two separatesystems, an assumption that thwarted the development of a unified model. The presentstudy outlines a signal-detection-based model of face-matching performance. The modelcan explain a myriad of face-matching phenomena, including the match-mismatchdissociation. The model is also capable of generating new predictions concerning the roleof confidence and similarity and their intricate relations with accuracy, all within theconfines of a single system. The new model was tested against six alternative competitorsmodels (some postulate discrete rather than continuous representations) in threeexperiments. Data analyses consisted of hierarchically-nested model fitting, ROC curveanalyses, and calibration curves analyses. All of the analyses provided substantialsupport in the signal-detection-based confidence-similarity model.

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