Facial Expression Recognition with Convolutional Neural Networks

Singh, Shekhar; Nasoz, Fatma

doi:10.1109/ccwc47524.2020.9031283

Cited by 70 publications

(28 citation statements)

References 34 publications

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“…Compared to the related works, the provided approach obtains a good recognition rate with fewer features and more recognized facial expressions. However, researchers in [4][5][6] obtained a good recognition rate ranged (60%-70%) but using different number of features (17,20,8) respectively with different classifiers. Also, researchers in [7,8] could gain a high accuracy using large number of features but with the ability to recognize fewer expressions from different dataset.…”

Section: Performance Evaluation and Resultsmentioning

confidence: 99%

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Classification techniques’ performance evaluation for facial expression recognition

Mahmood

Abdulrazaq

Zeebaree

et al. 2020

IJEECS

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<p><span>Facial exprestion recognition as a recently developed method in computer vision is founded upon the idea of analazing the facial changes in which are witnessed due to emotional impacts on an individual. This paper provides a performance evaluation of a set of supervised classifiers used for facial expression recognition based on minimum features selected by chi-square. These features are the most iconic and influential ones that have tangible value for result dermination. The highest ranked six features are applied on six classifiers including multi-layer preceptron, support vector machine, decision tree, random forest, radial baised function, and k-nearest neioughbor to figure out the most accurate one when the minum number of features are utilized. This is done via analyzing and appraising the classifiers’ performance. CK+ is used as the research’s dataset. Random forest with the total accuracy ratio of 94.23 % is illustrated as the most accurate classifier amongst the rest. </span></p>

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Section: Performance Evaluation and Resultsmentioning

confidence: 99%

“…The research illustrated 60% to 70% rate of facial recognition accuracy rate for the whole JAFEE database. Singh and Nasoz [8] demonstrated an original method to enhance future accuracy concerning the process f face recognition via reprocessing. It embeds two major steps of face detection and correction of the illumination.…”

Section: Introductionmentioning

confidence: 99%

Classification techniques’ performance evaluation for facial expression recognition

Mahmood

Abdulrazaq

Zeebaree

et al. 2020

IJEECS

View full text Add to dashboard Cite

show abstract

“…In addition, Moetesum et al [47] used CNN to extract visual features from different samples of handwritten images and a support vector machine (SVM) classifier was used for classification with an accuracy of 83%. Singh and Nasoz [48] also worked on handwritten images to decrease the loss function of the images from the validation set. Classification accuracy reached 83.11% and 90.38%, for meander and spiral tests, respectively, using CNNs and SVM.…”

Section: Parkinson's Disease Diagnosismentioning

confidence: 99%

Data Clustering Improves Siamese Neural Networks Classification of Parkinson’s Disease

2021

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Parkinson’s disease (PD) is a clinical neurodegenerative disease having symptoms like tremor, rigidity, and postural disability. According to Harvard, about 60,000 of American citizens are diagnosed with PD yearly, with more than 10 million people infected worldwide. An estimate of 4% of the people have PD before they reach the age 50; however, the incident increases with age. Diagnosis of PD relies on the expertise of the physician and depends on several established clinical criteria. This makes the diagnosis subjective and inefficient. Hence, continuous efforts are being made to enhance the diagnosis of PD using deep learning approaches that rely on experienced neurologists. Siamese neural networks mainly work on two different input vectors and are used in comparison of output vectors. Moreover, clustering a dataset before applying classification enhances the distribution of similar samples among groups. In addition, applying the Siamese network can overcome the limitation of samples per class in the dataset by guiding the network to learn differences between samples rather than focusing on learning specific classes. In this paper, a Siamese neural network is applied to diagnose PD. Siamese networks predict the sample class by estimating how similar a sample is to other samples. The idea behind this work is clustering the dataset before training the network, as different pairs that belong to the same cluster are candidates to be mistaken by the network and assumed to be matched pairs. To overcome this problem, the dataset is first clustered, and then the architecture feeds the network to pairs of the same cluster. The proposed framework is concerned with comparing the performance when using clustered against unclustered data. The proposed framework outperforms the conventional framework without clustering. The accuracy achieved for classifying unclustered PD patients reached 76.75%, while it reached 84.02% for clustered data, outperforming the same technique on unclustered data. The significance of this study is in the enhanced performance achieved due to the clustering of data, which shows a promising framework to enhance the diagnostic capability of computer-aided disease diagnostic tools.

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“…The authors tested their model with the RAVDEES dataset and, compared to decision tree, random forest and SVM, CNN showed 78.20 percent accuracy in identifying emotions. In 29 , without needing any pre-processing or feature extraction tasks, the authors demonstrate the classification of FER based on static images, using CNNs. In a seven-class classification assignment, the authors obtained a test accuracy of 61.7 percent on FER2013 compared to 75.2 percent in the state-of-the-art classification.…”

Section: Literature Reviewsmentioning

confidence: 99%

Four-Layer ConvNet to Facial Emotion Recognition with Minimal Epochs and the Significance of Data Diversity

Debnath¹,

Reza²,

Rahman³

et al. 2021

Preprint

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Emotion recognition defined as identifying human emotion and is directly related to different fields such as human-computer interfaces, human emotional processing, irrational analysis, medical diagnostics, data-driven animation, human-robot communi- cation and many more. The purpose of this study is to propose a new facial emotional recognition model using convolutional neural network. Our proposed model, “ConvNet”, detects seven specific emotions from image data including anger, disgust, fear, happiness, neutrality, sadness, and surprise. This research focuses on the model’s training accuracy in a short number of epoch which the authors can develop a real-time schema that can easily fit the model and sense emotions. Furthermore, this work focuses on the mental or emotional stuff of a man or woman using the behavioral aspects. To complete the training of the CNN network model, we use the FER2013 databases, and we test the system’s success by identifying facial expressions in the real-time. ConvNet consists of four layers of convolution together with two fully connected layers. The experimental results show that the ConvNet is able to achieve 96% training accuracy which is much better than current existing models. ConvNet also achieved validation accuracy of 65% to 70% (considering different datasets used for experiments), resulting in a higher classification accuracy compared to other existing models. We also made all the materials publicly accessible for the research community at: https://github.com/Tanoy004/Emotion-recognition-through-CNN.

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Facial Expression Recognition with Convolutional Neural Networks

Abstract: Agradezco a profesores y amigos que han compartido conmigo nuevos conocimientos y enseñanzas académicas para seguir valorando lo que hago día a día.

Cited by 70 publications

References 34 publications

Classification techniques’ performance evaluation for facial expression recognition

Classification techniques’ performance evaluation for facial expression recognition

Data Clustering Improves Siamese Neural Networks Classification of Parkinson’s Disease

Four-Layer ConvNet to Facial Emotion Recognition with Minimal Epochs and the Significance of Data Diversity

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