Real Time System for Facial Analysis
This work addresses the need for efficient facial analysis systems in applications like human-computer interaction, but it is incremental as it combines existing methods without major innovations.
The authors tackled the problem of real-time facial analysis by developing a system that recognizes age, gender, and facial expression using convolutional neural networks, achieving unspecified accuracy on common datasets.
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.