  Liveliness detection acts as a safe guard against spoofing attacks. Most of
the researchers used vision based techniques to detect liveliness of the user,
but they are highly sensitive to illumination effects. Therefore it is very
hard to design a system, which will work robustly under all circumstances.
Literature shows that most of the research utilize eye blink or mouth movement
to detect the liveliness, while the other group used face texture to
distinguish between real and imposter. The classification results of all these
approaches decreases drastically in variable light conditions. Hence in this
paper we are introducing fuzzy expert system which is sufficient enough to
handle most of the cases comes in real time. We have used two testing
parameters, (a) under bad illumination and (b) less movement in eyes and mouth
in case of real user to evaluate the performance of the system. The system is
behaving well in all, while in first case its False Rejection Rate (FRR) is
0.28, and in second case its FRR is 0.4.
