  This paper is focused on the automatic extraction of persons and their
attributes (gender, year of born) from album of photos and videos. We propose
the two-stage approach, in which, firstly, the convolutional neural network
simultaneously predicts age/gender from all photos and additionally extracts
facial representations suitable for face identification. We modified the
MobileNet, which is preliminarily trained to perform face recognition, in order
to additionally recognize age and gender. In the second stage of our approach,
extracted faces are grouped using hierarchical agglomerative clustering
techniques. The born year and gender of a person in each cluster are estimated
using aggregation of predictions for individual photos. We experimentally
demonstrated that our facial clustering quality is competitive with the
state-of-the-art neural networks, though our implementation is much
computationally cheaper. Moreover, our approach is characterized by more
accurate video-based age/gender recognition when compared to the publicly
available models.
