M. N. Kavitha1,*, A. RajivKannan2
Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 689-704, 2023, DOI:10.32604/iasc.2023.025437
Abstract Facial Expression Recognition (FER) has been an important field of research for several decades. Extraction of emotional characteristics is crucial to FERs, but is complex to process as they have significant intra-class variances. Facial characteristics have not been completely explored in static pictures. Previous studies used Convolution Neural Networks (CNNs) based on transfer learning and hyperparameter optimizations for static facial emotional recognitions. Particle Swarm Optimizations (PSOs) have also been used for tuning hyperparameters. However, these methods achieve about 92 percent in terms of accuracy. The existing algorithms have issues with FER accuracy and precision. Hence, the overall FER performance is… More >
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