A signal-detection-based confidence-similarity model of face-matching

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 setting 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 responses are governed by two
separate systems, an assumption that likely thwarted the development of a unified model
of face-matching. The present study proposes 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
analyses to better understand the relations between match and mismatch responses, and
their relations to factors of confidence and similarity. The model can account for the
presence of both within-identity and between-identity 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.