Towards a Real-Time Facial Analysis System

TL;DR

This paper presents a real-time facial analysis system that combines multiple deep neural networks to recognize age, gender, facial expression, and similarity, achieving high accuracy and speed suitable for practical applications.

Contribution

It introduces a system-level design integrating parallelized neural networks for comprehensive facial analysis in real-time, including a novel multitask network for multiple attribute prediction.

Findings

System achieves real-time recognition speed.

Accuracy comparable to state-of-the-art methods.

Multitask network effectively predicts multiple attributes.

Abstract

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.