  The area of face recognition is one of the most widely researched areas in
the domain of computer vision and biometric. This is because, the non-intrusive
nature of face biometric makes it comparatively more suitable for application
in area of surveillance at public places such as airports. The application of
primitive methods in face recognition could not give very satisfactory
performance. However, with the advent of machine and deep learning methods and
their application in face recognition, several major breakthroughs were
obtained. The use of 2D Convolution Neural networks(2D CNN) in face recognition
crossed the human face recognition accuracy and reached to 99%. Still, robust
face recognition in the presence of real world conditions such as variation in
resolution, illumination and pose is a major challenge for researchers in face
recognition. In this work, we used video as input to the 3D CNN architectures
for capturing both spatial and time domain information from the video for face
recognition in real world environment. For the purpose of experimentation, we
have developed our own video dataset called CVBL video dataset. The use of 3D
CNN for face recognition in videos shows promising results with DenseNets
performing the best with an accuracy of 97% on CVBL dataset.
