Real Time Emotion Recognition from Facial Expressions Using CNN Architecture

Özdemir, Mehmet Akif; Elagoz, Berkay; Alaybeyoǧlu, Ayşegül; Sadighzadeh, Reza; Akan, Aydın

doi:10.1109/tiptekno.2019.8895215

Cited by 60 publications

(23 citation statements)

References 14 publications

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“…The pictures' pixel values have also been transformed into 64×64 gray images to be put in neural networks. Also, this is required to prevent the excess density of the neural networks [28]. In a real-life situation, data collection for images can be captured in various conditions, such as different directions, locations, sizes, and visibility.…”

Section: Facial Expression Recognition (Fer)mentioning

confidence: 99%

“…Thus in such raw images, the conventional pre-processing technique such as standardization, cutting, and centralization enhances images' recognition during any experimental period [29]. • Feature extraction: The primary step is to extract facial features from the image or video input [28], [30].…”

Section: Facial Expression Recognition (Fer)mentioning

confidence: 99%

“…Ozdemir et al in [28] suggested the low-cost and practical approach of classifying in real-time seven different emotions based on LeNet CNN architecture (happy, sad, shocked, furious, hideous, terrified, and neutral). Where facial expression pictures, of which a limited number can be said, have been educated on CNN and have attained a high degree of accuracy.…”

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: Facial Expression Recognition (Fer)mentioning

confidence: 99%

Section: Facial Expression Recognition (Fer)mentioning

confidence: 99%

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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“…There are varieties of applications for facial emotions recognition like student classroom behavior monitoring system, airport/railway suspicious person detection system, autism children expression detection, facial expression-based emotion chat applications, real-time person pain monitoring systems, etc. (2,3) .…”

Section: Introductionmentioning

confidence: 99%

“…In the transfer learning method, pre-trained models like (AlexNet (13) , VGG-S (14) , VGG-M (14) , and VGG-VD16 (15) ) are used to extract the low-level features using "MatConvNet" toolkit to predict the human activity in the surveillance video camera. The author, Mehmet Akif OZDEMIR (2) identified seven emotions by training the LeNet architecture and obtained a validation accuracy of 91.81%. The CNN model is adopted by…”

Section: Introductionmentioning

confidence: 99%

Real-time video based emotion recognition using convolutional neural network and transfer learning

Sujanaa¹,

Palanivel²

2020

IJST

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Background/Objectives: The deep learning approaches have paved their way to construct various artificial intelligence products and the proposed system uses a convolutional neural network for detecting real-time emotions of mankind. The objective of the study is to develop a real-time application for emotion recognition using convolutional neural network and transfer learning methods. Methods/Statistical analysis: The proposed system considers happy, normal and surprised categories of emotions. The system consists of four major steps: dataset collection, training, validation, and real-time testing. The dataset is comprised of face images containing emotions such as happy, normal and surprised in the form of video frames. The face and mouth regions are detected using the Haar-Based Cascade classifier at 20 frames per second. Findings: The convolutional neural network (CNN) is trained using mouth images and the pre-trained models VGG16 and VGG19 are trained with face images. The trained model is used to detect the emotions in the live webcam video. The experimental results show that the CNN model trained using mouth images gives an accuracy of 85.71% and the pre-trained models trained with face images using transfer learning method achieves an accuracy of 77.78%. The proposed system using CNN outperforms the pre-trained models for recognizing the emotions in real-time video. Novelty/Applications: The proposed system is entirely based on the mouth region video frames and the real-time emotion recognition system is developed. This work can detect the three emotions in an unconstrained laboratory environment.

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