الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
The COVID-19 pandemic has caused significant challenges in various research areas, and researchers have had to adapt their methods and approaches to continue their work in a rapidly changing environment.
The COVID-19 pandemic has led to widespread use of face masks, which can significantly impact facial expression recognition. Facial expression recognition is a field of study that uses computer vision and machine learning techniques to detect and interpret emotions based on facial features. Face masks cover a significant portion of the face, including the mouth and nose, which are critical for conveying emotions. This means that facial expression recognition algorithms may not work as effectively on individuals wearing masks. Automatic Facial Expression Recognition (AFER), also known as emotional recognition, is important for understanding and responding to social cues. However, the use of face masks during the COVID-19 pandemic has presented challenges for automatic methods, as they were not designed to work with masked faces. Machine learning techniques have been effective in detecting emotions in unmasked faces but have achieved poor recognition rates with masked faces.