  With the advent of 2-dimensional Convolution Neural Networks (2D CNNs), the
face recognition accuracy has reached above 99%. However, face recognition is
still a challenge in real world conditions. A video, instead of an image, as an
input can be more useful to solve the challenges of face recognition in real
world conditions. This is because a video provides more features than an image.
However, 2D CNNs cannot take advantage of the temporal features present in the
video. We therefore, propose a framework called $Sf_{3}CNN$ for face
recognition in videos. The $Sf_{3}CNN$ framework uses 3-dimensional Residual
Network (3D Resnet) and A-Softmax loss for face recognition in videos. The use
of 3D ResNet helps to capture both spatial and temporal features into one
compact feature map. However, the 3D CNN features must be highly discriminative
for efficient face recognition. The use of A-Softmax loss helps to extract
highly discriminative features from the video for face recognition. $Sf_{3}CNN$
framework gives an increased accuracy of 99.10% on CVBL video database in
comparison to the previous 97% on the same database using 3D ResNets.
