  Faces play a magnificent role in human robot interaction, as they do in our
daily life. The inherent ability of the human mind facilitates us to recognize
a person by exploiting various challenges such as bad illumination, occlusions,
pose variation etc. which are involved in face recognition. But it is a very
complex task in nature to identify a human face by humanoid robots. The recent
literatures on face biometric recognition are extremely rich in its application
on structured environment for solving human identification problem. But the
application of face biometric on mobile robotics is limited for its inability
to produce accurate identification in uneven circumstances. The existing face
recognition problem has been tackled with our proposed component based
fragmented face recognition framework. The proposed framework uses only a
subset of the full face such as eyes, nose and mouth to recognize a person.
It's less searching cost, encouraging accuracy and ability to handle various
challenges of face recognition offers its applicability on humanoid robots. The
second problem in face recognition is the face spoofing, in which a face
recognition system is not able to distinguish between a person and an imposter
(photo/video of the genuine user). The problem will become more detrimental
when robots are used as an authenticator. A depth analysis method has been
investigated in our research work to test the liveness of imposters to
discriminate them from the legitimate users. The implication of the previous
earned techniques has been used with respect to criminal identification with
NAO robot. An eyewitness can interact with NAO through a user interface. NAO
asks several questions about the suspect, such as age, height, her/his facial
shape and size etc., and then making a guess about her/his face.
