Automatic facial expression recognition: A survey based on feature extraction and classification techniques

Kauser, Nazima; Sharma, Jitendra

doi:10.1109/ictbig.2016.7892679

Cited by 8 publications

(3 citation statements)

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“…Highest accuracy is achieved through Neural Network (NN) as a classifier and the blend of Gabor Wavelet (GW) and Local Binary Pattern (LBP) is likewise great with a precision of 91.2 and 90%. The other techniques like Principal Component Analysis and Support Vector Machine's exhibition were low as discussed in [11].…”

Section: Literature Surveymentioning

confidence: 99%

Image Verification and Emotion Detection using Effective Modelling Techniques

Sumana¹,

Vaishnavi²

2022

IJACSA

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The feelings expressed on the face reflect the manner of thinking and provide useful insights of happenings inside the brain. Face Detection enables us to identify a face. Recognizing the facial expressions for different emotions is to familiarize the machine with human like capacity to perceive and identify human feelings, which involves classifying the given input images of the face into one of the seven classes which is achieved by building a multi class classifier. The proposed methodology is based on convolutional neural organizations and works on 48x48 pixel-based grayscale images. The proposed model is tested on various images and gives the best accuracy when compared with existing functionalities. It detects faces in images, recognizes them and identifies emotions and shows improved performance because of data augmentation. The model is experimented with varying depths and pooling layers. The best results are obtained sequential model of six layers of Convolutional Neural Network and softmax activation function applied to last layer. The approach works for real time data taken from videos or photos.

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Section: Literature Surveymentioning

confidence: 99%

Image Verification and Emotion Detection using Effective Modelling Techniques

Sumana¹,

Vaishnavi²

2022

IJACSA

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“…It involves the splitting up of each video clip into frames and from each of these frames, various features can be extracted. There are many automatic methods to detect facial expressions in videos and images which were given in Happy and Routray (2015) and Kauser and Sharma (2017). Body gesture features are very much important in recognizing the sentiment.…”

Section: Video/image Feature Extractionmentioning

confidence: 99%

Multimodal sentimental analysis for social media applications: A comprehensive review

Chandrasekaran

Nguyen

Hemanth

2021

WIREs Data Min & Knowl

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The analysis of sentiments is essential in identifying and classifying opinions regarding a source material that is, a product or service. The analysis of these sentiments finds a variety of applications like product reviews, opinion polls, movie reviews on YouTube, news video analysis, and health care applications including stress and depression analysis. The traditional approach of sentiment analysis which is based on text involves the collection of large textual data and different algorithms to extract the sentiment information from it. But multimodal sentimental analysis provides methods to carry out opinion analysis based on the combination of video, audio, and text which goes a way beyond the conventional text-based sentimental analysis in understanding human behaviors. The remarkable increase in the use of social media provides a large collection of multimodal data that reflects the user's sentiment on certain aspects. This multimodal sentimental analysis approach helps in classifying the polarity (positive, negative, and neutral) of the individual sentiments. Our work aims to present a survey of recent developments in analyzing the multimodal sentiments (involving text, audio, and video/image) which involve humanmachine interaction and challenges involved in analyzing them. A detailed survey on sentimental dataset, feature extraction algorithms, data fusion methods, and efficiency of different classification techniques are presented in this work.

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“…Facial emotion recognition (FER) and speech emotion recognition (SER) can be used to build human computer interfaces, which are used in different applications in different fields, including the following [5]- [9]: a) In the medical field for disease and pain detection. b) In the psychological field, such as lie detection, autism and depression as well as the creation of appropriate therapeutic applications.…”

Section: Introductionmentioning

confidence: 99%

Emotion Recognition System Based on Hybrid Techniques

Abdulsalam¹,

Alhamdani²,

Abdullah³

2019

IJMLC

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Emotion recognition has important applications in human-computer interaction. Various sources such as facial expressions and speech have been considered for interpreting human emotions.The aim of this paper is to develop an emotion recognition system from facial expressions and speech using a hybrid of machine-learning algorithms in order to enhance the overall performance of human computer communication.For facial emotion recognition, a deep convolutional neural network is used for feature extraction and classification, whereas for speech emotion recognition, the zero-crossing rate, mean, standard deviation and mel frequency cepstral coefficient features are extracted. The extracted features are then fed to a random forest classifier.In addition, a bi-modal system for recognising emotions from facial expressions and speech signals is presented. This is important since one modality may not provide sufficient information or may not be available for any reason beyond operator control. To perform this, decision-level fusion is performed using a novel way for weighting according to the proportions of facial and speech impressions. The results show an average accuracy of 93.22 %.

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Automatic facial expression recognition: A survey based on feature extraction and classification techniques

Cited by 8 publications

References 10 publications

Image Verification and Emotion Detection using Effective Modelling Techniques

Image Verification and Emotion Detection using Effective Modelling Techniques

Multimodal sentimental analysis for social media applications: A comprehensive review

Emotion Recognition System Based on Hybrid Techniques

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