  Facial recognition is fundamental for a wide variety of security systems
operating in real-time applications. In video surveillance based face
recognition, face images are typically captured over multiple frames in
uncontrolled conditions; where head pose, illumination, shadowing, motion blur
and focus change over the sequence. We can generalize that the three
fundamental operations involved in the facial recognition tasks: face
detection, face alignment and face recognition. This study presents comparative
benchmark tables for the state-of-art face recognition methods by testing them
with same backbone architecture in order to focus only on the face recognition
solution instead of network architecture. For this purpose, we constructed a
video surveillance dataset of face IDs that has high age variance, intra-class
variance (face make-up, beard, etc.) with native surveillance facial imagery
data for evaluation. On the other hand, this work discovers the best
recognition methods for different conditions like non-masked faces, masked
faces, and faces with glasses.
