  In this paper we propose the two-stage approach of organizing information in
video surveillance systems. At first, the faces are detected in each frame and
a video stream is split into sequences of frames with face region of one
person. Secondly, these sequences (tracks) that contain identical faces are
grouped using face verification algorithms and hierarchical agglomerative
clustering. Gender and age are estimated for each cluster (person) in order to
facilitate the usage of the organized video collection. The particular
attention is focused on the aggregation of features extracted from each frame
with the deep convolutional neural networks. The experimental results of the
proposed approach using YTF and IJB-A datasets demonstrated that the most
accurate and fast solution is achieved for matching of normalized average of
feature vectors of all frames in a track.
