Comparative Analysis of AI-Based Facial Identification and Expression Recognition Using Upper and Lower Facial Regions

Abstract

The COVID-19 pandemic has significantly impacted society, having led to a lack of social skills in children who became used to interacting with others while wearing masks. To analyze this issue, we investigated the effects of masks on face identification and facial expression recognition, using deep learning models for these operations. The results showed that when using the upper or lower facial regions for face identification, the upper facial region allowed for an accuracy of 81.36%, and the lower facial region allowed for an accuracy of 55.52%. Regarding facial expression recognition, the upper facial region allowed for an accuracy of 39% compared to 49% for the lower facial region. Furthermore, our analysis was conducted for a number of facial expressions, and specific emotions such as happiness and contempt were difficult to distinguish using only the upper facial region. Because this study used a model trained on data generated from human labeling, it is assumed that the effects on humans would be similar. Therefore, this study is significant because it provides engineering evidence of a decline in facial expression recognition; however, wearing masks does not cause difficulties in identification.

Keywords

• face identification
• facial expression recognition
• face cropping
• emotion
• masked face