A Scalable Method for Real-Time Facial Emotion Recognition using an Artificial Neural Network and Polynomial Equation

Abstract

Facial emotion recognition has recently attracted considerable interest due to its wide range of applications. It plays a crucial role in supporting individuals with autism spectrum disorders and improving interactions between humans and computers. The ability to execute these applications in real-time is essential. The architecture of the model and the computational resources available are the key determinants of inference time. Consequently, the development of a real-time solution requires a concentrated effort on these elements. In this paper, we present a scalable approach that utilizes EfficientNetV2, chosen for its operational efficiency. Our methodology involves resolution scaling based on a polynomial equation, which ensures real-time performance across various computational resources and model configurations. This scalable technique employs a polynomial equation to identify the optimal resolution for designated inference times, specifically adapted to our hardware and model specifications. By implementing the polynomial equation for resolution scaling, we created two variants of EfficientNetV2. Our findings from the KDEF dataset indicate that the proposed EfficientNetV2 can accurately classify images in real time on our hardware.

Keywords

• real-time facial emotion recognition
• deep learning
• efficientnetv2
• imbalanced datasets
• resolution scaling