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How Different is Different? Criterion and Sensitivity in Face-Space

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TLDR
It is concluded that both criterion and sensitivity need to be considered and that an angular similarity metric based on standardized PC values provides the best metric for specifying what physical differences will be perceived to change in identity.
Abstract
Not all detectable differences between face images correspond to a change in identity. Here we measure both sensitivity to change and the criterion difference that is perceived as a change in identity. Both measures are used to test between possible similarity metrics. Using a same/different task and the method of constant stimuli criterion is specified as the 50% “different” point (P50) and sensitivity as the Difference Limen (DL). Stimuli and differences are defined within a “face-space” based on Principal Components Analysis (PCA) of measured differences in three-dimensional shape. In Experiment 1 we varied views available. Criterion (P50) was lowest for identical full-face view comparisons that can be based on image differences. When comparing across views P50, was the same for a static 45o change as for multiple animated views, although sensitivity (DL) was higher for the animated case, where it was as high as for identical views. Experiments 2 and 3 tested possible similarity metrics. Experiment 2 contrasted Euclidean and Mahalanobis distance by setting PC1 or PC2 to zero. DL did not differ between conditions consistent with Mahalanobis. P50 was lower when PC2 changed emphasising that perceived changes in identity are not determined by the magnitude of Euclidean physical differences. Experiment 3 contrasted a distance with an angle based similarity measure. We varied the distinctiveness of the faces being compared by varying distance from the origin, a manipulation that affects distances but not angles between faces. Angular P50 and DL were both constant for faces from 1 to 2 standard deviations from the mean, consistent with an angular measure. We conclude that both criterion and sensitivity need to be considered and that an angular similarity metric based on standardised PC values provides the best metric for specifying what physical differences will be perceived to change in identity.

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References
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Eigenfaces for recognition

TL;DR: A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.
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Detection Theory: A User's Guide

TL;DR: This book discusses Detection and Discrimination of Compound Stimuli: Tools for Multidimensional Detection Theory and Multi-Interval Discrimination Designs and Adaptive Methods for Estimating Empirical Thresholds.
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A morphable model for the synthesis of 3D faces

TL;DR: A new technique for modeling textured 3D faces by transforming the shape and texture of the examples into a vector space representation, which regulates the naturalness of modeled faces avoiding faces with an “unlikely” appearance.
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