Self-aware face emotion accelerated recognition algorithm: a novel neural network acceleration algorithm of emotion recognition for international students

Abstract

With an increasing number of human-computer interaction application scenarios, researchers are looking for computers to recognize human emotions more accurately and efficiently. Such applications are desperately needed at universities, where people want to understand the students’ psychology in real time to avoid catastrophes. This research proposed a self-aware face emotion accelerated recognition algorithm (SFEARA) that improves the efficiency of convolutional neural networks (CNNs) in the recognition of facial emotions. SFEARA will recognize that critical and non-critical regions of input data perform high-precision computation and convolutive low-precision computation during the inference process, and finally combine the results, which can help us get the emotional recognition model for international students. Based on a comparison of experimental data, the SFEARA algorithm has 1.3× to 1.6× higher computational efficiency and 30% to 40% lower energy consumption than conventional CNNs in emotion recognition applications, is better suited to the real-time scenario with more background information.

Keywords

• Emotion recognition
• International students
• Hybrid accuracy
• Acceleration neural network
• Computational efficiency