Development of deep learning-based facial expression recognition system

Jung, Heechul; Lee, Sihaeng; Park, Sunjeong; Kim, Byungju; Kim, Junmo; Lee, Injae; Ahn, Chang‐Beom

doi:10.1109/fcv.2015.7103729

Cited by 55 publications

(21 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They evaluate the impact of normalization and synthetic data augmentation on classification accuracy using the extended CK dataset (CK+), and achieve average classification accuracy of 91.46% for 6 class CNN recognition (max 93.74%). Lopes, De Aguiar and Oliveira-Santos, (2015) found that augmenting training data through the addition of random perturbations and horizontal flips enhanced overall accuracy, a finding supported by Jung et al, (Jung, 2015). However, despite achieving high classification accuracy, the system proposed by Lopes, De Aguiar and Oliveira-Santos, (2015) requires the locations of each eye prior to image normalization.…”

Section: Cnns For Facial Expression Recognitionmentioning

confidence: 92%

Human Centric Facial Expression Recognition

Clawson

Delicato

Bowerman

2018

Electronic Workshops in Computing

View full text Add to dashboard Cite

Facial expression recognition (FER) is an area of active research, both in computer science and in behavioural science. Across these domains there is evidence to suggest that humans and machines find it easier to recognise certain emotions, for example happiness, in comparison to others. Recent behavioural studies have explored human perceptions of emotion further, by evaluating the relative contribution of features in the face when evaluating human sensitivity to emotion. It has been identified that certain facial regions have more salient features for certain expressions of emotion, especially when emotions are subtle in nature. For example, it is easier to detect fearful expressions when the eyes are expressive. Using this observation as a starting point for analysis, we similarly examine the effectiveness with which knowledge of facial feature saliency may be integrated into current approaches to automated FER. Specifically, we compare and evaluate the accuracy of 'full-face' versus upper and lower facial area convolutional neural network (CNN) modelling for emotion recognition in static images, and propose a human centric CNN hierarchy which uses regional image inputs to leverage current understanding of how humans recognise emotions across the face. Evaluations using the CK+ dataset demonstrate that our hierarchy can enhance classification accuracy in comparison to individual CNN architectures, achieving overall true positive classification in 93.3% of cases.

show abstract

Section: Cnns For Facial Expression Recognitionmentioning

confidence: 92%

Human Centric Facial Expression Recognition

Clawson

Delicato

Bowerman

2018

Electronic Workshops in Computing

View full text Add to dashboard Cite

show abstract

“…It is used as a feature extraction method on images which contain faces and it is also used as a face recognition method. [1][4] [5] Neural network is a network of neuron or artificial nodes. They are useful in clustering and classifying data.…”

Section: Literature Surveymentioning

confidence: 99%

Facial Expression Recognition using Compressed Images

Akshay¹,

Mandara²,

Rao³

2019

IJRTE

View full text Add to dashboard Cite

Facial expression plays an integral part in expressing ones emotions. These expressions can be conveyed in various forms such as happiness, sadness, anger, surprise, fear, disgust, and neutral. So we propose a system which recognizes and classifies these expressions. We have used an image compression method (FMM) to compress the images collected. Then we detect human face in the compressed image using Viola Jones Haar-like object detector. Using the detected face, we extract the facial features that changes with the changing expressions using LBP. Finally we classify the expression using the extraction using k-NN. Presently, FER is applied in a wide variety of environments including robotics, mobile application, digital signs, as a psychiatric tool for verifying the observations made by the psychologist, etc. The existing system use grayscale/ RGB images which consumes a lot of space and requires a lot of computational time. We present a new approach of using compressed images to reduce the space and time required.

show abstract

“…In the same year, Lv Y., et al detected the face components by deep belief networks, and then did the facial expression recognition using autoencoder [8]. In 2015, H Jung, et al developed a facial expression recognition system using deep neural network and convolutional neural network [9]. In the same year, Liu P, et al proposed the combination of deep belief networks and AdaBoost method for facial expression recognition [10].…”

Section: Introductionmentioning

confidence: 99%

Facial Expression Recognition Method Based on Stacked Denoising Autoencoders and Feature Reduction

Zhao¹,

Yan²,

Yang³

et al. 2017

dtetr

View full text Add to dashboard Cite

Based on the deep learning theory, a novel facial expression recognition method, which utilizes both Principal Component Analysis (PCA) and stacked denoising autoencoders (SDAE), is proposed in this paper. At first, PCA is used as a linear dimensionality reduction method on the expression features, and subsequently non-linear dimensionality is further learnt by a greed layer-wise method of stacked denoising autoencoders. Then, some meaningful and low-dimensioned expression features can be learnt and used to classify. The comparative experiment results show that the proposed method is more effective than some other expression recognition methods based on deep learning theories and it can also get higher expression recognition accuracy than traditional non-deep learning based expression recognition methods.

show abstract

Development of deep learning-based facial expression recognition system

Cited by 55 publications

References 6 publications

Human Centric Facial Expression Recognition

Human Centric Facial Expression Recognition

Facial Expression Recognition using Compressed Images

Facial Expression Recognition Method Based on Stacked Denoising Autoencoders and Feature Reduction

Contact Info

Product

Resources

About