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Hybrid Convolutional Neural Network and Long Short-Term Memory Approach for Facial Expression Recognition

Intelligent Automation & Soft Computing 2023年第1期35卷 689-704页

作者： M.N.Kavitha A.RajivKannan Department of Computer Science&Engineering Kongu Engineering CollegePerunduraiErode638060Tamil NaduIndia Department of Computer Science&Engineering K.S.R.College of EngineeringTiruchengode637215Tamil NaduIndia

Facial Expression Recognition(FER)has been an importantfield of research for several *** of emotional characteristics is crucial to FERs,but is complex to process as they have significant intra-class *** characteristics have not been completely explored in static *** studies used Convolution Neural Networks(CNNs)based on transfer learning and hyperparameter optimizations for static facial emotional *** Swarm optimizations(PSOs)have also been used for tuning ***,these methods achieve about 92 percent in terms of *** existing algorithms have issues with FER accuracy and ***,the overall FER performance is degraded *** address this issue,this work proposes a combination of CNNs and Long Short-Term Memories(LSTMs)called the HCNN-LSTMs(Hybrid CNNs and LSTMs)approach for *** work is evaluated on the benchmark dataset,Facial Expression Recog Image Ver(FERC).Viola-Jones(VJ)algorithms recognize faces from preprocessed images followed by HCNN-LSTMs feature extractions and FER ***,the success rate of Deep Learning Techniques(DLTs)has increased with hyperparameter tunings like epochs,batch sizes,initial learning rates,regularization parameters,shuffling types,and *** proposed work uses improved weight based whale optimization algorithms(IWWOAs)to select near-optimal settings for these parameters using bestfitness *** experi-mentalfindings demonstrated that the proposed HCNN-LSTMs system outper-forms the existing methods.

关键词： Facial expression recognition Gaussianfilter hyperparameter optimization improved weight-based whale optimization algorithm deep learning(DL)

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