Comparison Study of Automated Facial Expression Recognition Models
Resumo
Facial expressions play a crucial role in human nonverbal communication, and in the psychology field there is a strong consensus on the existence of five key emotions: anger, fear, disgust, sadness, and happiness. This paper aims to evaluate multiple facial expression recognition detection models, assessing their performance across different machines and databases. By identifying the strengths and weaknesses of each option, the study seeks to comparatively determine the most suitable model for specific tasks or scenarios. For each computer, all databases were processed through the usage of the detection models, while measuring the required runtime for the facial expression detection. The detection models: Residual Masking Network and Deepface, were tested through the databases Extended Cohn-Kanade and AffectNet. The assessed data point towards an average higher accuracy for the model Residual Masking Network, but faster runtime for Deepface. Thereby, Deepface may be preferentially employed in scenarios where time constraints are a primary concern, there is limited processing capability available, or an emphasis on recognizing either happiness or neutral expressions, while Residual Masking Network might be favored in striving for a higher detection accuracy.
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