Facial Emotion Recognition Using Deep Convolutional Neural Network

Pranav, E.; Kamal, Suraj; Chandran, C. Satheesh; Supriya, M. H.

doi:10.1109/icaccs48705.2020.9074302

Cited by 136 publications

(46 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The neural network backpropagates the errors of the network to adjust the weights, which in turn reduces the error (loss) function. The weight adjustment is made using equation ( 2) [5].…”

Section: B Facial Emotion Detection Systemsmentioning

confidence: 99%

“…The field of facial expression analysis is a fascinating and challenging issue with implications in various fields, including human-computer interactions and medical applications. The pervasiveness is because computers are generally faster for computational analysis and are also a source of cheaper and more dynamic labor [4], [5]. These computers will have to understand human emotions to better relate to humans in most instances.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Deep Learning Algorithms in Facial Emotion Detection and Recognition

Baffour

Nunoo‐Mensah

Keelson

et al. 2022

Inf. J. Ilm. Bid. Teknol. Inf. dan Komun.

View full text Add to dashboard Cite

Facial emotion recognition (FER) forms part of affective computing, where computers are trained to recognize human emotion from human expressions. Facial Emotion Recognition is very necessary for bridging the communication gap between humans and computers because facial expressions are a form of communication that transmits 55% of a person's emotional and mental state in a total face-to-face communication spectrum. Breakthroughs in this field also make computer systems (robotic systems) better serve or interact with humans. Research has far advanced for this cause, and Deep learning is at its heart. This paper systematically discusses state-of-the-art deep learning architectures and algorithms for facial emotion detection and recognition. The paper also reveals the dominance of CNN architectures over other known architectures like RNNs and SVMs, highlighting the contributions, model performance, and limitations of the reviewed state-of-the-art. It further identifies available opportunities and open issues worth considering by various FER research in the future. This paper will also discover how computation power and availability of large facial emotion datasets have also limited the pace of progress.

show abstract

“…The neural network backpropagates the errors of the network to adjust the weights, which in turn reduces the error (loss) function. The weight adjustment is made using equation ( 2) [5].…”

Section: B Facial Emotion Detection Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Survey on Deep Learning Algorithms in Facial Emotion Detection and Recognition

Baffour

Nunoo‐Mensah

Keelson

et al. 2022

Inf. J. Ilm. Bid. Teknol. Inf. dan Komun.

View full text Add to dashboard Cite

show abstract

“…Deep learning can automatically learn arbitrary complex mappings from inputs to outputs and support multiple inputs and outputs, making it the preferred approach when handling complex, large datasets. Deep learning architectures include convolutional neural networks, artificial neural networks (ANN) and recurrent neural networks (RNNs), which are used in different fields depending on the desired output (Pranav et al 2020).…”

Section: Deep Learningmentioning

confidence: 99%

Forecasting water consumption on transboundary water resources for water resource management using the feed-forward neural network: a case study of the Nile River in Egypt and Kenya

Mumbi

Bavumiragira

et al. 2021

Mar. Freshwater Res.

View full text Add to dashboard Cite

Water resources are an essential component of a country’s natural resource potential. Pressure on these resources is set to increase due to increased water demand, climate change and rainfall variability. This could lead to conflicts between sectoral users, within or between countries, especially among transboundary countries. Interest in transboundary water resources is a priority, especially where issues such as uncertainty regarding the status of transboundary waterbodies and reductions in water volume persist. In this study, we used the feed-forward neural network to forecast water demand along the Nile River in two countries, Egypt and Kenya. Two scenarios were modelled. Input data for the first scenario included preceding records of precipitation, gross domestic product, population and water use in the agricultural sector. The second scenario observed the effects of the growing economy on water resources by doubling the gross domestic product and keeping all other inputs constant. For Kenya, the results projected a steady increase in water demand throughout the next 20 years for both scenarios. However, for Egypt, the observed trend in both scenarios was a decline in water demand, followed by a steady increase. The results underscore the importance of forecasting for easier future planning and management, and to help governing bodies along transboundary water resources develop timely strategies in the future to alleviate future water shortages and poor management of water resources.

show abstract

“…Li and Deng [8] report that deep FER consists of several steps including face alignment detectors (e.g., Viola-Jones [9], face alignment 3000fps [10]), data augmentation (e.g., rotation, skew, scaling), face normalization, feature learning, and emotion classification. Several deep neural network models have been used to learn image features such as Convolutional Neural Network (CNN) [11,12], the hybrid Convolution -RNN [13], and Generative Ad-versarial Network [14]. Human facial expressions are usually classified into 7 categories [15]: afraid, angry, disgusted, happy, neutral, sad, and surprised.…”

Section: Introductionmentioning

confidence: 99%

Facial Expression Recognition and Image Description Generation in Vietnamese

Lam

Nguyen

Nguy

et al. 2021

Frontiers in Artificial Intelligence and Applications

View full text Add to dashboard Cite

This paper discusses a facial expression recognition model and a description generation model to build descriptive sentences for images and facial expressions of people in images. Our study shows that YOLOv5 achieves better results than a traditional CNN for all emotions on the KDEF dataset. In particular, the accuracies of the CNN and YOLOv5 models for emotion recognition are 0.853 and 0.938, respectively. A model for generating descriptions for images based on a merged architecture is proposed using VGG16 with the descriptions encoded over an LSTM model. YOLOv5 is also used to recognize dominant colors of objects in the images and correct the color words in the descriptions generated if it is necessary. If the description contains words referring to a person, we recognize the emotion of the person in the image. Finally, we combine the results of all models to create sentences that describe the visual content and the human emotions in the images. Experimental results on the Flickr8k dataset in Vietnamese achieve BLEU-1, BLEU-2, BLEU-3, BLEU-4 scores of 0.628; 0.425; 0.280; and 0.174, respectively.

show abstract

Facial Emotion Recognition Using Deep Convolutional Neural Network

Cited by 136 publications

References 9 publications

A Survey on Deep Learning Algorithms in Facial Emotion Detection and Recognition

A Survey on Deep Learning Algorithms in Facial Emotion Detection and Recognition

Forecasting water consumption on transboundary water resources for water resource management using the feed-forward neural network: a case study of the Nile River in Egypt and Kenya

Facial Expression Recognition and Image Description Generation in Vietnamese

Contact Info

Product

Resources

About