This study presents the Web-Agile Facial Emotion Recognition and Eye-Tracking System (WAFER-ET), which combines AI-based facial expression recognition and eye-tracking models. Eye tracking is implemented using WebGazer.js, and facial expression recognition using a custom lightweight CNN-based model (CLCM). Experimental analysis showed effective performance in the system's real-time processing and data streaming. The low delay between the experimental and real-time results platforms and low memory load were achieved. Moreover, integrating facial expression recognition and eye tracking in the WAFER-ET system enables different fields of application
Web-Agile Facial Emotion Recognition and Eye-Tracking System (WAFER-ET)
Duradoni, Mirko;
2023-01-01
Abstract
This study presents the Web-Agile Facial Emotion Recognition and Eye-Tracking System (WAFER-ET), which combines AI-based facial expression recognition and eye-tracking models. Eye tracking is implemented using WebGazer.js, and facial expression recognition using a custom lightweight CNN-based model (CLCM). Experimental analysis showed effective performance in the system's real-time processing and data streaming. The low delay between the experimental and real-time results platforms and low memory load were achieved. Moreover, integrating facial expression recognition and eye tracking in the WAFER-ET system enables different fields of applicationI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
