  Face detection and recognition has been prevalent with research scholars and
diverse approaches have been incorporated till date to serve purpose. The
rampant advent of biometric analysis systems, which may be full body scanners,
or iris detection and recognition systems and the finger print recognition
systems, and surveillance systems deployed for safety and security purposes
have contributed to inclination towards same. Advances has been made with
frontal view, lateral view of the face or using facial expressions such as
anger, happiness and gloominess, still images and video image to be used for
detection and recognition. This led to newer methods for face detection and
recognition to be introduced in achieving accurate results and economically
feasible and extremely secure. Techniques such as Principal Component analysis
(PCA), Independent component analysis (ICA), Linear Discriminant Analysis
(LDA), have been the predominant ones to be used. But with improvements needed
in the previous approaches Neural Networks based recognition was like boon to
the industry. It not only enhanced the recognition but also the efficiency of
the process. Choosing Backpropagation as the learning method was clearly out of
its efficiency to recognize nonlinear faces with an acceptance ratio of more
than 90% and execution time of only few seconds.
