  Facial morphs created between two identities resemble both of the faces used
to create the morph. Consequently, humans and machines are prone to mistake
morphs made from two identities for either of the faces used to create the
morph. This vulnerability has been exploited in "morph attacks" in security
scenarios. Here, we asked whether the "other-race effect" (ORE) -- the human
advantage for identifying own- vs. other-race faces -- exacerbates morph attack
susceptibility for humans. We also asked whether face-identification
performance in a deep convolutional neural network (DCNN) is affected by the
race of morphed faces. Caucasian (CA) and East-Asian (EA) participants
performed a face-identity matching task on pairs of CA and EA face images in
two conditions. In the morph condition, different-identity pairs consisted of
an 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 face
images. Moreover, CA participants showed an advantage for CA faces in
comparison to EA faces (a partial ORE). Of primary interest, morph
identification was substantially worse for cross-race faces than for own-race
faces. Similar to humans, the DCNN performed more accurately for original face
images than for morphed image pairs. Notably, the deep network proved
substantially more accurate than humans in both cases. The results point to the
possibility that DCNNs might be useful for improving face identification
accuracy when morphed faces are presented. They also indicate the significance
of the ORE in morph attack susceptibility in applied settings.
