  In this paper, we analyze some of our real-world deployment of face
recognition (FR) systems for various applications and discuss the gaps between
expectations of the user and what the system can deliver. We evaluate some of
our proposed algorithms with ad-hoc modifications for applications such as FR
on wearable devices (like Google Glass), monitoring of elderly people in senior
citizens centers, FR of children in child care centers and face matching
between a scanned IC/passport face image and a few live webcam images for
automatic hotel/resort checkouts. We describe each of these applications, the
challenges involved and proposed solutions. Since FR is intuitive in nature and
we human beings use it for interactions with the outside world, people have
high expectations of its performance in real-world scenarios. However, we
analyze and discuss here that it is not the case, machine recognition of faces
for each of these applications poses unique challenges and demands specific
research components so as to adapt in the actual sites.
