An improved boosting algorithm and its application to facial emotion recognition

Lee, Chien-Cheng; Shih, Cheng-Yuan; Lai, Wen-Ping; Lin, Po-Chiang

doi:10.1007/s12652-011-0085-8

Cited by 25 publications

(9 citation statements)

References 11 publications

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“…Zhi and Ruan [17] facial feature vectors derived from 2D discriminant locality preservation projections. Lee et al [18] employed a boosting method to classify the data and extend the wavelet transform to 2D, which it is known as the contourlet transform (CT), for the feature extraction procedure from the picture. Changand Huang [19] integrated face recognition into the FER system to improve each person's skill at expression recognition.…”

Section: Machine Learning-based Fer Approachmentioning

confidence: 99%

Transfer Learning Technique with EfficientNet for Facial Expression Recognition System

Alam¹,

Kartowisastro²,

Wicaksono³

2022

RIA

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Facial Expression Recognition (FER) systems are helpful in a wide range of industries, including healthcare, social marketing, targeted advertising, the music industry, school counseling systems, and detection in the police sector. In this research, using Deep Convolutional Neural Networks (DCNN) architecture, specifically from the EfficientNet family (EfficientNet-B0, Efficient-Net-B01, EfficientNet-B02, EfficientNet-B03, EfficientNet-B04, EfficientNet-B05, EfficientNet-B06, and EfficientNet-B07) has previously gone through a combined scaling process of combined dept, width and resolution. First, the previously frozen sublayer EfficientNet model was used for feature extraction. Next, the layer closer to the output layer is melted by several layers to be retrained in order to recognize the pattern of the CK+ and JAFFE data sets. This process is called the transfer learning technique. This technique is very powerful for working on relatively small data sets, namely CK+ and JAFFE. The main of this research is to improve the accuracy and performance of facial expression recognition models with a transfer learning approach using EfficientNet pre-trained with fine-tuning strategy. Our proposed method, specifically using the EfficientNet-B0 architecture, achieves superior performance for each of the CK+ and JAFFE datasets, achieving 99.57% and 100% accuracy in the test set respectively.

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Section: Machine Learning-based Fer Approachmentioning

confidence: 99%

Transfer Learning Technique with EfficientNet for Facial Expression Recognition System

Alam¹,

Kartowisastro²,

Wicaksono³

2022

RIA

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“…To the best of our knowledge, most emotion classification studies have used data collected from healthy people 33,34,[57][58][59][60][61][62][63][64][65][66][67][68][69][70][71][72][73][74][75]. Among these, 27 studies used EEG data to classify emotions [6][7][8][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][27][28][29][30][31]33,34,75].…”

Section: Emotion Classification On Healthy Peoplementioning

confidence: 99%

“…To the best of our knowledge, most emotion classification studies have used data collected from healthy people [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 33 , 34 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 ]. Among these, 27 studies used EEG data to classify emotions [ 6 , 7 , 8 , 10 , 11 , 12 , …”

Section: Introductionmentioning

confidence: 99%

EEG-Based Emotion Classification for Alzheimer’s Disease Patients Using Conventional Machine Learning and Recurrent Neural Network Models

Seo

Laine

et al. 2020

Sensors

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As the number of patients with Alzheimer’s disease (AD) increases, the effort needed to care for these patients increases as well. At the same time, advances in information and sensor technologies have reduced caring costs, providing a potential pathway for developing healthcare services for AD patients. For instance, if a virtual reality (VR) system can provide emotion-adaptive content, the time that AD patients spend interacting with VR content is expected to be extended, allowing caregivers to focus on other tasks. As the first step towards this goal, in this study, we develop a classification model that detects AD patients’ emotions (e.g., happy, peaceful, or bored). We first collected electroencephalography (EEG) data from 30 Korean female AD patients who watched emotion-evoking videos at a medical rehabilitation center. We applied conventional machine learning algorithms, such as a multilayer perceptron (MLP) and support vector machine, along with deep learning models of recurrent neural network (RNN) architectures. The best performance was obtained from MLP, which achieved an average accuracy of 70.97%; the RNN model’s accuracy reached only 48.18%. Our study results open a new stream of research in the field of EEG-based emotion detection for patients with neurological disorders.

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“…SVM has been used for pattern recognition of biological features (Shivajirao et al 2014;Lee et al 2012;Khandoker et al 2009). SVM can solve nonlinear problems in a linear space because of the nonlinear feature mapping of the kernelinduced feature space function.…”

Section: Svmmentioning

confidence: 99%

Modified EMG-based handgrip force prediction using extreme learning machine

Sun

Zhang

2015

Soft Comput

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Various myoelectric prostheses controlled by electromyography (EMG) signals have been developed. However, there have been few studies that provide fast and accurate methods to predict handgrip force from EMG signals. Rapid and precise handgrip force prediction is required, especially for the real-time control system of myoelectric prostheses. In this study, extreme learning machine (ELM) is applied to predict handgrip force from surface EMG signals of forearm muscles. Furthermore, ELM is compared with support vector machine (SVM) and multiple nonlinear regression (MNLR). The below 10 % of the surface EMG and handgrip force signals were cut away, and then the root mean square feature extracted from the modified surface EMG signals was taken as input vector for these three kinds of predicting mechanisms. For the testing dataset, ELM achieved a slightly larger root mean squared error than SVM did and a smaller one than MNLR did. Meanwhile, all three methods showed high correlation coefficients. For the total processing time, ELM and MNLR consumed much less time than SVM did. Experimental results demonstrate that ELM possesses a relatively good accuracy and little consumed time, although SVM is effective for handgrip force estimation in terms of accuracy. Overall, ELM has a promising potential for predicting handgrip force rapidly and precisely.

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An improved boosting algorithm and its application to facial emotion recognition

Cited by 25 publications

References 11 publications

Transfer Learning Technique with EfficientNet for Facial Expression Recognition System

Transfer Learning Technique with EfficientNet for Facial Expression Recognition System

EEG-Based Emotion Classification for Alzheimer’s Disease Patients Using Conventional Machine Learning and Recurrent Neural Network Models

Modified EMG-based handgrip force prediction using extreme learning machine

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