Towards a Real-Time Facial Analysis System
This work addresses the need for efficient, multi-task facial analysis systems for real-time applications, but it is incremental as it builds on existing deep learning methods without introducing a fundamentally new paradigm.
The authors tackled the problem of building a real-time facial analysis system by designing a system-level approach that integrates multiple tasks like age, gender, expression, and similarity recognition, achieving comparable accuracy to state-of-the-art methods while meeting real-time speed requirements.
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.