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Facial morphs created between two identities resemble both of the faces usedto create the morph. Consequently, humans and machines are prone to mistakemorphs made from two identities for either of the faces used to create themorph. This vulnerability has been exploited in "morph attacks" in securityscenarios. Here, we asked whether the "other-race effect" (ORE) -- the humanadvantage for identifying own- vs. other-race faces -- exacerbates morph attacksusceptibility for humans. We also asked whether face-identificationperformance in a deep convolutional neural network (DCNN) is affected by therace of morphed faces. Caucasian (CA) and East-Asian (EA) participantsperformed a face-identity matching task on pairs of CA and EA face images intwo conditions. In the morph condition, different-identity pairs consisted ofan image of identity "A" and a 50/50 morph between images of identity "A" and"B". In the baseline condition, morphs of different identities never appeared.As expected, morphs were identified mistakenly more often than original faceimages. Moreover, CA participants showed an advantage for CA faces incomparison to EA faces (a partial ORE). Of primary interest, morphidentification was substantially worse for cross-race faces than for own-racefaces. Similar to humans, the DCNN performed more accurately for original faceimages than for morphed image pairs. Notably, the deep network provedsubstantially more accurate than humans in both cases. The results point to thepossibility that DCNNs might be useful for improving face identificationaccuracy when morphed faces are presented. They also indicate the significanceof the ORE in morph attack susceptibility in applied settings.

The Influence of the Other-Race Effect on Susceptibility to Face Morphing Attacks