  In this paper we describe the anatomy of a real-time facial analysis system.
The system recognizes the age, gender and facial expression from users in
appearing in front of the camera. All components are based on convolutional
neural networks, whose accuracy we study on commonly used training and
evaluation sets. A key contribution of the work is the description of the
interplay between processing threads for frame grabbing, face detection and the
three types of recognition. The python code for executing the system uses
common libraries--keras/tensorflow, opencv and dlib--and is available for
download.
