Emotion recognition from facial expressions using hybrid feature descriptors

Kalsum, Tehmina; Anwar, Syed Muhammad; Majid, Muhammad; Khan, Bilal; Ali, Sahibzada Muhammad

doi:10.1049/iet-ipr.2017.0499

Cited by 51 publications

(19 citation statements)

References 29 publications

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“…Appearance feature describes the change in face texture such as bulges, forefront, wrinkles, region surrounding the mouth and eyes. The appearance‐based technique uses transformation techniques like Principle Component Analysis, Independent Component Analysis, Linear Discriminate Analysis and so on and statistical approaches like Local Binary Pattern, Wavelets and hybrid features to represent the facial feature.…”

Section: Related Workmentioning

confidence: 99%

Bimodal recognition of affective states with the features inspired from human visual and auditory perception system

Arivazhagan¹,

Russel²

2019

Int J Imaging Syst Tech

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In this article, attention‐based mechanism with the enhancement on biologically inspired network for emotion recognition is proposed. Existing bio‐inspired models use multiscale and multiorientation architecture to gain discriminative power and to extract meticulous visual features. Prevailing HMAX model represents S2 layers by randomly selected prototype patches from training samples that increase the computational complexity and degrade the discerning ability. As eyes and mouth regions are the most powerful and reliable cues in determining facial emotions, they serve as the prototype patches for S2 layer in HMAX model. Audio code 4 book is constructed from mel‐frequency cepstral coefficients, temporal and spectral features processed by principal component analysis. Audio and video data features are fused to train support vector machine classifier. The attained results on eNTERFACE, surrey audio‐visual expressed emotion and acted facial expressions in the wild database datasets ascertain the efficiency of the proposed architecture for emotion recognition.

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Section: Related Workmentioning

confidence: 99%

Bimodal recognition of affective states with the features inspired from human visual and auditory perception system

Arivazhagan¹,

Russel²

2019

Int J Imaging Syst Tech

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“…SVM is extended to classify multi class labels using kernel functions. SVM classifies the data by hyperplanes [1]. It is used for linear as well as non-linear data.…”

Section: Existing Workmentioning

confidence: 99%

“…data are close to each other. It takes the k value as input from the user, and then calculates the distance between the points to point that belongs to k and forms a kind of graph [1]. The disadvantage with this is the value of the k need to be specified by the user before itself which may not lead to accurate results.…”

Section: Existing Workmentioning

confidence: 99%

Emotion Recognition using Feed Forward Neural Network & Naïve Bayes

Shahane*¹,

.K²,

Siddeeq³

2019

IJITEE

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In this paper we analyze and predict the emotion of a user by recognizing his/her face. Face recognition is a software application which is used to identify a particular person; it will be mostly useful in security applications to secure our data. Now a day we are using face unlock in mobiles to unlock our phones. We need to know the emotions of a person in some situations. Though we can recognize his emotion through his tone of voice, it would be more helpful if get to know his emotions. This can be much helpful in finding out a criminal by finding out his emotion whether he is feeling nervous or not which expresses his/her fear by this. In order to analyze his/her emotion firstly we need to recognize his/her face, so we need to use face recognition method and then implement emotion analysis. Here we use different algorithms to implement emotion analysis such as CNN. We will have the dataset with pixels and emotion this will be the training data. Then we will be initially taking the picture and then convert them to pixels these will be acting as the testing data. We then use an algorithm to predict these pixels emotion which is nothing but predicting the emotion of the picture taken

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“…In Artificial Intelligent era, facial expression recognition (FER) is interesting and challenging task with the problems of limited dataset, different environments, pose, occlusion, person variation etc. FER systems have been applied many systems such as human-computer-interaction (HCI), games, animation of data-driven, surveillance, clinical monitoring etc., [1]. Ekman and Friesen, psychologists from America defined six universal facial expressions: fear, happiness, anger, disgust, surprise, and sadness and also explored Action Units based facial action coding system (FACS) to describe facial features of expressions [2].…”

Section: Introductionmentioning

confidence: 99%

Feature extraction and classification methods of facial expression: a survey

Htay

2021

Comput. Sci. Inf. Technol.

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Facial Expression is a significant role in affective computing and one of the non-verbal communication for human computer interaction. Automatic recognition of human affects has become more challenging and interesting problem in recent years. Facial Expression is the significant features to recognize the human emotion in human daily life. Facial Expression Recognition System (FERS) can be developed for the application of human affect analysis, health care assessment, distance learning, driver fatigue detection and human computer interaction. Basically, there are three main components to recognize the human facial expression. They are face or face’s components detection, feature extraction of face image, classification of expression. The study proposed the methods of feature extraction and classification for FER.

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Emotion recognition from facial expressions using hybrid feature descriptors

Cited by 51 publications

References 29 publications

Bimodal recognition of affective states with the features inspired from human visual and auditory perception system

Bimodal recognition of affective states with the features inspired from human visual and auditory perception system

Emotion Recognition using Feed Forward Neural Network & Naïve Bayes

Feature extraction and classification methods of facial expression: a survey

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