Facial Expression Recognition

Kulkarni, Ketki R.; Bagal, S. B.

doi:10.1109/indicon.2015.7443572

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…FER is a type of pattern recognition problem which usually consists of two modules, Facial feature extraction and classification [1]. In the FER system, after feature extraction, the extracted features are classified.…”

Section: Facial Expression Recognitionmentioning

confidence: 99%

“…A facial expression can be considered as a visible attestation of inner state of mind and hence gives idea about intention, interest, and psychology of that person. As a result of the huge information carried by facial expressions, they play vital role in human interaction with humans as well as machine [1]. Facial expression recognition can be used as a vital component in natural interface between human and machine.…”

Section: Introductionmentioning

confidence: 99%

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Emotion Detection from Facial Expression using Support Vector Machine

Jain¹,

Aggarwal²,

Kumar³

et al. 2017

IJCA

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The objective of this paper is to apply Support Vector Machine to the problem of classifying emotion on images of human faces. This well defined problem is complicated by the natural variation in people's faces, requiring the classification algorithm to distinguish the small number of relevant features from the large pool of input features. Three different kernels i.e., linear kernel, polynomial kernel and RBF kernel are used to recognise eight facial expressions, anger, contempt, disgust, fear, happiness, neutral, sadness and surprise of human beings in still images. Accuracy of the three kernels is compared to judge the best kernel for facial expression recognition.

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Section: Facial Expression Recognitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Emotion Detection from Facial Expression using Support Vector Machine

Jain¹,

Aggarwal²,

Kumar³

et al. 2017

IJCA

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show abstract

“…Kulkarni & Bagal[42] Perform a comparative study of automatic Facial Expression Recognition by compensating for the in uence of age on the recognition procedure. The proposed study uses the Log Gabor lter and Gabor lter for feature mining followed by the implementation of an SVM classi er for training and classi cation purposes.…”

mentioning

confidence: 99%

Database Development and Recognition of Facial Expression using Deep Learning

Bapat

Patil

Mali

2023

Preprint

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Facial expressions reflect human emotions and an individual's intentions. To detect facial expressions by human beings is a very easy task whereas it’s a very difficult task using computers. They perform a vigorous part in everyday life. It is a non-verbal mode that may include feelings, opinions, and thoughts without speaking. Deep neural networks, Convolutional Neural Networks, Neural networks, Artificial Intelligence, Fuzzy Logic, and Machine Learning are the different technologies used to detect facial expressions. To detect facial expressions, static images, video, webcam data, or real-time images can be used. This research paper focused on developing the SMM Facial Expression dataset and proposes a convolutional neural network model to identify facial expressions. The proposed method was tested on two different benchmarked datasets namely FER2013 and CK+ for facial expression detection. We have explored the proposed model on CK+ and achieved 93.94% accuracy and 67.18 % for FER2013 respectively. To analyze and test the accuracy of the proposed model, we have implemented it on the SMM Facial Expression dataset and achieved 96.60% of accuracy.

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Emotion Recognition and Discrimination of Facial Expressions using Convolutional Neural Networks

Manewa

Mayurathan

2020

2020 IEEE 8th R10 Humanitarian Technology Conference (R10-Htc)

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Facial Expression Recognition

Cited by 6 publications

References 10 publications

Emotion Detection from Facial Expression using Support Vector Machine

Emotion Detection from Facial Expression using Support Vector Machine

Database Development and Recognition of Facial Expression using Deep Learning

Emotion Recognition and Discrimination of Facial Expressions using Convolutional Neural Networks

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