Facial Emotion Recognition Analysis Based on Age-Biased Data

Abstract

This paper aims to analyze the importance of age-biased data in recognizing six emotions using facial expressions. For this purpose, a custom dataset (adults, kids, mixed) was constructed using images that separated the existing datasets (FER2013 and MMA FACILE EXPRESSION) into adults (≥14) and kids (≤13). The convolutional Neural Networks (CNN) algorithm was used to calculate emotion recognition accuracy. Additionally, this study investigated the effect of the characteristics of CNN architecture on emotion recognition accuracy. Based on the variables of Accuracy and FLOP, three types of CNN architectures (MobileNet-V2, SE-ResNeXt50 (32 × 4 d), and ResNeXt-101 (64 × 4 d)) were adopted. As for the experimental result, SE-ResNeXt50 (32 × 4 d) showed the highest accuracy at 79.42%, and the model that learned by age obtained 22.24% higher accuracy than the model that did not learn by age. In the results, the difference in expression between adults and kids was greatest for fear and neutral emotions. This study presented valuable results on age-biased learning data and algorithm type effect on emotion recognition accuracy.

Emotion Detection Based on Pupil Variation

“…Currently, many emotion recognition systems exist that are able to detect, process, and classify the emotional states of a human subject. Most of them operate by obtaining various physiological signals, facial expressions, and voice under exposure to specific stimuli, with the purpose of recognizing specific emotions [5][6][7][8][9][10]. Physiological signals, such as electromyogram, electrocardiography, etc.)…”