A Face Expression Recognition Using CNN &amp; LBP

Ravi, R.; Yadhukrishna, S.V; prithviraj, Rajalakshmi

doi:10.1109/iccmc48092.2020.iccmc-000127

Cited by 50 publications

(18 citation statements)

References 10 publications

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“…On the other hand, most of the reviewed methods were based on classification approaches. Where these methods used several classification algorithms such as SVM in [66] and [58], SDM in [14], in some reviewed papers, more than one classifier was used such as SVM, LDA, and KNN in [37], also LDA, MLP, SVM and DT in [22]. Nevertheless, this article observed that the SVM algorithm's use is the best among all the algorithms used to increase accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…In [34] and the attention mechanism, the writer used LBP to develop the attention model to achieve better outcomes. CNN and LBP were used in research [58]. Results show that CNN, with its built-in classifier (softmax), is better than LBP in terms of accuracy, there was 97.32% with the CK+ dataset, but with YALE FACE, it was low, about 31.8 %.…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, many feature extraction methods have been reviewed in this study. Among them, two researchers ( [34] and [58]) depended on LBP. In [34] and the attention mechanism, the writer used LBP to develop the attention model to achieve better outcomes.…”

Section: Discussionmentioning

confidence: 99%

“…Ravi et al [58] made a fair contrast between two of the most widely used FER techniques and shed some light on their accuracy. LBP and CNN are the approaches being used here.…”

Section: Facial Expression Recognition Surveymentioning

confidence: 99%

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Facial Expression Recognition Based on Deep Learning Convolution Neural Network: A Review

Abdullah

Abdulazeez

2021

JSCDM

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Facial emotional processing is one of the most important activities in effective calculations, engagement with people and computers, machine vision, video game testing, and consumer research. Facial expressions are a form of nonverbal communication, as they reveal a person's inner feelings and emotions. Extensive attention to Facial Expression Recognition (FER) has recently been received as facial expressions are considered. As the fastest communication medium of any kind of information. Facial expression recognition gives a better understanding of a person's thoughts or views and analyzes them with the currently trending deep learning methods. Accuracy rate sharply compared to traditional state-of-the-art systems. This article provides a brief overview of the different FER fields of application and publicly accessible databases used in FER and studies the latest and current reviews in FER using Convolution Neural Network (CNN) algorithms. Finally, it is observed that everyone reached good results, especially in terms of accuracy, with different rates, and using different data sets, which impacts the results.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…Ravi et al [58] made a fair contrast between two of the most widely used FER techniques and shed some light on their accuracy. LBP and CNN are the approaches being used here.…”

Section: Facial Expression Recognition Surveymentioning

confidence: 99%

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Facial Expression Recognition Based on Deep Learning Convolution Neural Network: A Review

Abdullah

Abdulazeez

2021

JSCDM

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“…A weighted mixture deep neural network (WMDNN) is developed in [6] to recognize the facial expressions by using a weighted fusion of the partial VGG16 and shallow CNN. Reference [7] has implemented a shallow CNN without any pre-processing step on different publicly available datasets like CK+ (97.32%), JAFFE (77.27%) and YALE FACE and obtained the recognition rate of only 31.82%. A descriptive survey on deep FER can be found in [8].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network Based Facial Expression Recognition Using Image Filtering Techniques

Agarwal¹,

Mittal²,

Hanmandlu³

2021

IJIES

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Facial expressions are indicative of one's mood that serves to communicate his/her the status of mind. Facial expression recognition poses problems when we attempt to use Convolutional Neural Network (CNN) architectures. The choice of a particular CNN architecture/model and the design of its internal architecture are two problems that need to be addressed. We have chosen a simple CNN architecture comprising two convolutional layers and two pooling layers. To improve its performance the input facial images displaying emotions are subject to two kinds of filtering: One by Gaussian filter and another by LoG. The classification of seven emotions by the CNN model on the filtered facial images from Extended Cohn-Kanade (CK+) dataset comprising 981 images has led to the attainment of 100% recognition accuracy thus vindicating the effectiveness of the proposed approach.

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