Convolution neural network for automatic facial expression recognition

Chen, Xiaoguang; Yang, Xuan; Wang, Maosen; Zou, Jiancheng

doi:10.1109/icasi.2017.7988558

Cited by 50 publications

(14 citation statements)

References 9 publications

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“…Approach: From this section, different approaches towards FER based on Deep Learning can be learned. (Chen, et al, 2017) (Kumar, et al, 2019) proposed a new CNN structure for FER, and they used CK+ and JAFFE databases for experiments. The detected faces are cropped using openCV library and the facial features are extracted with CNN using deep learning.…”

Section: Deep Learning Based Facial Emotion Recognitionmentioning

confidence: 99%

A Review on Facial Emotion Recognition and Classification Analysis with Deep Learning

Jaison¹

2021

Biosc Biotech Res Comm

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Automatic face expression recognition is an exigent research subject and a challenge in computer vision. It is an interdisciplinary domain standing at the crossing of behavioural science, psychology, neurology, and artificial intelligence. Human-robot interaction is getting more significant with the automation of every field, like treating autistic patients, child therapy, babysitting, etc. In all the cases robots need to understand the present state of mind for better decision making. It is difficult for machine learning techniques to recognize the expressions of people since there will be significant changes in the way of their expressions. The emotions expressed through the human face have its importance in making arguments and decisions on different subjects. Machine Learning with Computer Vision and Deep Learning can be used to recognize facial expressions from the preloaded or real time images with human faces. DNN (Deep Neural Networking) is one among the hottest areas of research and is found to be very effective in classification of images with a high degree of accuracy. In the proposed work, the popular dataset CK+ is analysed for comparison. The dataset FER 2013 and home-brewed data sets are used in the work for calculating the accuracy of the model created. The results are obtained in such a way that DCNN approach is very efficient in facial emotion recognition. Experiments and study show that the dataset, FER 2013 is a high-quality dataset with equal efficiency as the other two popular datasets. This paper aims to ameliorate the accuracy of classification of facial emotion.

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Section: Deep Learning Based Facial Emotion Recognitionmentioning

confidence: 99%

A Review on Facial Emotion Recognition and Classification Analysis with Deep Learning

Jaison¹

2021

Biosc Biotech Res Comm

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“…-Neural Network AND Assistance technology -Artificial Neural Networks AND Visual disability -Artificial Intelligence AND Visual disability Diversas aplicaciones para la asistencia de personas con discapacidad visual integran el uso de sistemas entrenados con RNA y la adquisición de imágenes con cámaras para el reconocimiento de patrones [45,46]. La detección de diferentes tipos de objetos como puertas, esquinas, bordes, caminos, entre otros que facilitan el reconocimiento de obstáculos [47][48][49] y el reconocimiento de rostros y expresiones faciales para mejorar las interacciones sociales [50].…”

Section: Ayudas Integrando Visión Por Computador Y Redes Neuronales Aunclassified

Dispositivos de asistencia para la movilidad en personas con discapacidad visual: una revisión bibliográfica

Salazar

Mesa

2019

Rev. politec.

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Este documento presenta una revisión literaria, de los dispositivos de ayuda en la movilidad para personas con discapacidad visual, con el objetivo de obtener una visión clara, sobre el progreso de la tecnología y técnicas empleadas para asistir a esta población. De esta forma, se pretende obtener pautas básicas para analizar los equipos más relevantes, para ayudar a las personas con visión reducida y destacar las mejoras que se podrían implementar. Los dispositivos más comunes, corresponden a la integración de diferentes sensores y componentes electrónicos en bastones, para aumentar su capacidad de detección de obstáculos. Además, se han desarrollado dispositivos con cámaras, que incluyen algoritmos de visión por computador y técnicas de Inteligencia Artificial, para mejorar los resultados y eficiencia de los equipos. Finalmente, se presentan las características fundamentales para los sistemas de asistencia, encontrando que aún no se cuenta con dispositivos que satisfagan las necesidades de los usuarios.

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“…Chen et.al [5] approach the Convolution Neural network for automatic identification of facial expression using techniques. When recognizing image preprocessing measures to extract only particular phrases from the face and prevent their issues with parameter-based convolution neural network.…”

Section: An Separation Of Different Facial Expression Recognition Systemmentioning

confidence: 99%

“…Then the performance comparison is made with the existing method and the proposed model as shown in figure 2 below and the results of the experiments show that the proposed model achieves substantial improvement in AU recognition than the existing model in that period. In [5] here they used to extend CK+ database which contains some video sequences in which each sequence comprises of about 10 to 30 frames and seven types of expressions categories: angry, disgust, neutral, sad, fear, surprise, happy. Each sequence begins with the neutral emotion and then the feelings of the image change through the respective sequence marks.…”

Section: Fig 1 Different Facial Expressionsmentioning

confidence: 99%

An Separation of Different Facial Expression Recognition System

2019

IJITEE

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Face recognition is one of the hot topics in the current world and one of the popular topics of computer studies. Today face recognition in the network society and access to digital data is gaining more attention. The facial recognition system technology is a biometric assessment of a human's face. There are many facial recognition techniques that are intended depending on facial expressions extraction, one of which is 3D facial recognition, as well as their fusion,is difficult. During preprocessing measures for picture recognition to remove only expression-specific characteristics from the face and prevent their issues with a convolution neural network. We can also use some theorems such as LBP and Taylor's theorem to model face recognition. In particular, for cloud robots, we can also use this facial recognition on robots. The robot can perform functions and share data between servers and devices. Seven fundamental expressions are used to identify and classify: happiness, shock, fear, disgust, sadness, rage, and a neutral condition. Until now, the recognition rate is quite up to the expectation stage, but it still tries to enhance. To enhance the recognition frequency of facial image recognition, feelings are chosen by the vibrant Bayesian network technique to depict the development of facial awareness in addition to various emotional operations of facial expressions. The ICCA techniques involve various multivariate sets of distinct facial features that could be eyes, nose, and mouth.

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Convolution neural network for automatic facial expression recognition

Cited by 50 publications

References 9 publications

A Review on Facial Emotion Recognition and Classification Analysis with Deep Learning

A Review on Facial Emotion Recognition and Classification Analysis with Deep Learning

Dispositivos de asistencia para la movilidad en personas con discapacidad visual: una revisión bibliográfica

An Separation of Different Facial Expression Recognition System

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