  Facial analysis is an active research area in computer vision, with many
practical applications. Most of the existing studies focus on addressing one
specific task and maximizing its performance. For a complete facial analysis
system, one needs to solve these tasks efficiently to ensure a smooth
experience. In this work, we present a system-level design of a real-time
facial analysis system. With a collection of deep neural networks for object
detection, classification, and regression, the system recognizes age, gender,
facial expression, and facial similarity for each person that appears in the
camera view. We investigate the parallelization and interplay of individual
tasks. Results on common off-the-shelf architecture show that the system's
accuracy is comparable to the state-of-the-art methods, and the recognition
speed satisfies real-time requirements. Moreover, we propose a multitask
network for jointly predicting the first three attributes, i.e., age, gender,
and facial expression. Source code and trained models are available at
https://github.com/mahehu/TUT-live-age-estimator.
