Abstract:
One of the major challenges of face recognition is to de-
sign a feature extractor that reduces the intra-class vari-
ations and increases the inter-class variations. The fea-
ture extraction algorithm has to be robust enough to extract
similar features for a particular class despite variations in
quality, pose, illumination, expression, aging and disguise.
The problem is exacerbated when there are two individuals
with lower inter-class variations, i.e., look-alikes. In such
cases, the intra-class similarity is higher than the inter-
class variation for these two individuals. This research
explores the problem of look-alikes faces and their effect
on human performance and automatic face recognition al-
gorithms. There is two fold contribution in this research:
firstly, we analyze human recognition capabilities for look-
alike appearances and secondly, compare it with automatic
face recognition algorithms. In our analysis, we observe
that neither humans nor automatic face recognition algo-
rithms are efficient for the challenge of look-alikes.
