Virtual Markers based Facial Emotion Recognition using ELM and PNN Classifiers

Murugappan, M.; Maruthapillai, Vasanthan; Wan, Khariunizam; Mutawa, A. M.; Sruthi, Sai; Yean, Chong Wen

doi:10.1109/cspa48992.2020.9068708

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…Based on the retrieved publications, Ayvaz et al [101], Candra Kirana et al [34], Dewan et al [35], El Hammoumi et al [31,32], Shi et al [42], Dash et al [45], Sabri [57], Murugappan et al [59], and Murugappan et al [60] have applied the Viola-Jones algorithm in the preprocessing step. Moreover, Ma et al [32], Yang et al [33], Hingu [62], and Zakka and Vadapalli [63] have used the Haar cascades method, while Hung et al [39], Lasri et al [40], and Alrayassi and Shilbayeh [50] have implemented the AdaBoost algorithm in the preprocessing phase.…”

Section: Preprocessing Methodsmentioning

confidence: 99%

“…Perform faster in classification [59] Takes more computational time and has a lower accuracy than PNN [59] Probabilistic neural network (PNN)…”

Section: Conventional Machine Learning Algorithmmentioning

confidence: 99%

“…Takes less computational time than ELM and is more efficient in classification [59] Possible overfitting of the data [59] Decision tree…”

Section: Conventional Machine Learning Algorithmmentioning

confidence: 99%

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Academic Emotion Classification Using FER: A Systematic Review

Lek

Teo

2023

Human Behavior and Emerging Technologies

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Facial emotion expressions are among the most potent, natural, and powerful means of human communication. Due to the COVID-19 pandemic, educational institutions worldwide are forced to switch rapidly to remote and online learning. Students are currently in an emergency state and must adapt to various and readily accessible learning methods, such as mobile learning applications or an e-learning system. A systematic literature review (SLR) is conducted to extract and synthesize information such as the emotion classifier used in the facial expression recognition (FER) system, the dataset used, the preprocessing technique applied, the feature extraction approach used, and the strength and limitation of the previous studies. Based on the search criteria, 701 publications were initially retrieved from five different digital databases, of which 48 studies have been chosen as primary studies for further analysis. Based on the findings of this study, the deep learning approach is the most frequently adopted approach in classifying student emotions during online learning. FER-2013 is the most commonly used FER dataset in FER studies, while DAiSEE is the most used academic emotion dataset. Moreover, support vector machine (SVM) is the conventional learning emotion classifier that is widely used in the FER systems, while convolutional neural network (CNN) is the most frequently used deep learning classifier. Next, it was found that the number of real-time FER systems is less than that of non-real-time FER systems. Finally, the top-1 accuracy of 94.6% was achieved by the long-term recurrent convolutional network on the academic emotion dataset, and the limitation is that it has low illumination and a lack of frontal pose.

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Section: Preprocessing Methodsmentioning

confidence: 99%

“…Perform faster in classification [59] Takes more computational time and has a lower accuracy than PNN [59] Probabilistic neural network (PNN)…”

Section: Conventional Machine Learning Algorithmmentioning

confidence: 99%

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Academic Emotion Classification Using FER: A Systematic Review

Lek

Teo

2023

Human Behavior and Emerging Technologies

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“…Most modern HCI systems still lack emotional intelligence and the ability to utilize these signals. An individual’s consciousness can easily influence and control non-physiological signals, but physiological signals represent their emotional state more accurately and consistently [ 4 , 5 ]. There are several types of physiological signals, such as electroencephalograms (EEGs), electrooculography (EOGs), electromyography (EMGs), etc.…”

Section: Introductionmentioning

confidence: 99%

Emotion Recognition from Spatio-Temporal Representation of EEG Signals via 3D-CNN with Ensemble Learning Techniques

et al. 2023

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The recognition of emotions is one of the most challenging issues in human–computer interaction (HCI). EEG signals are widely adopted as a method for recognizing emotions because of their ease of acquisition, mobility, and convenience. Deep neural networks (DNN) have provided excellent results in emotion recognition studies. Most studies, however, use other methods to extract handcrafted features, such as Pearson correlation coefficient (PCC), Principal Component Analysis, Higuchi Fractal Dimension (HFD), etc., even though DNN is capable of generating meaningful features. Furthermore, most earlier studies largely ignored spatial information between the different channels, focusing mainly on time domain and frequency domain representations. This study utilizes a pre-trained 3D-CNN MobileNet model with transfer learning on the spatio-temporal representation of EEG signals to extract features for emotion recognition. In addition to fully connected layers, hybrid models were explored using other decision layers such as multilayer perceptron (MLP), k-nearest neighbor (KNN), extreme learning machine (ELM), XGBoost (XGB), random forest (RF), and support vector machine (SVM). Additionally, this study investigates the effects of post-processing or filtering output labels. Extensive experiments were conducted on the SJTU Emotion EEG Dataset (SEED) (three classes) and SEED-IV (four classes) datasets, and the results obtained were comparable to the state-of-the-art. Based on the conventional 3D-CNN with ELM classifier, SEED and SEED-IV datasets showed a maximum accuracy of 89.18% and 81.60%, respectively. Post-filtering improved the emotional classification performance in the hybrid 3D-CNN with ELM model for SEED and SEED-IV datasets to 90.85% and 83.71%, respectively. Accordingly, spatial-temporal features extracted from the EEG, along with ensemble classifiers, were found to be the most effective in recognizing emotions compared to state-of-the-art methods.

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