Deep Explanation Model for Facial Expression Recognition Through Facial Action Coding Unit

Kim, Sun-Bin; Kim, Hyeoncheol

doi:10.1109/bigcomp.2019.8679370

Cited by 11 publications

(2 citation statements)

References 14 publications

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“…Transfer Learning can also be tried on these datasets [8] CK+ and JAFFE Dataset have very few images, and they are not Balanced, so making them balanced will give better testing results [1], [2]. FER based on Facial Action Units (AUs) has shown good results in a state of the art that can also be tried [10]. Some Advanced Models such as DBN, VAE, and GANs can be applied to improve accuracy [9], [11].…”

Section: Discussionmentioning

confidence: 99%

Deep learning for facial emotion recognition using custom CNN architecture

Borgalli¹,

Surve

2022

J. Phys.: Conf. Ser.

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Human facial expressions are an indication of true emotions. To recognize facial expressions accurately is useful in the field of Artificial Intelligence, Computing, Medical, e-Education, and many more. The facial expression recognition (FER) system detects emotion through facial expression. But, it is challenging to detect facial emotions accurately. However, recent advancements in technology, research, and availability of facial expression datasets have led to the development of many FER systems which can accurately detect facial emotions. Past research in the field of FER indicates With Convolutional Neural Networks (CNNs), deep learning techniques are the most advanced presently. Custom CNN Architecture is used to implement basic facial emotion recognition in static images in this paper. A K-fold cross-validation method was used to train them using FER13, CK+, and the JAFFE data set. On the seven classes of fundamental emotions, including anger, disgust, fear, happiness, neutrality, sorrow, and surprise, the FER13, CK+, and JAFFE datasets had an accuracy rate of 91.58 percent. Given the difficulty of developing unique CNN architecture, this study’s accurate findings contrast well with those of previous studies.

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Section: Discussionmentioning

confidence: 99%

Deep learning for facial emotion recognition using custom CNN architecture

Borgalli¹,

Surve

2022

J. Phys.: Conf. Ser.

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“…Deep learning (DL) algorithms have powerful feature learning abilities and have been used by various researchers to perform end-to-end learning in recent years [6], [7]. However, despite being a powerful feature learning tool, DL algorithms are strongly affected by high inter-subject variations that exist due to attributes such as race, level of expressiveness, and ethnicity, etc., [8], [9] that are non-linearly co-founded with emotion categorization.…”

Section: Introductionmentioning

confidence: 99%

An Out-of-Distribution Attack Resistance Approach to Emotion Categorization

Shehu

Browne

Eisenbarth

2021

IEEE Trans. Artif. Intell.

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Deep neural networks are a powerful model for feature extraction. They produce features that enable state-of-theart performance on many tasks, including emotion categorization. However, their homogeneous representation of knowledge has made them prone to attacks, i.e. small modification in train or test data to mislead the models. Emotion categorization can usually be performed to be either in-distribution (train and test with the same dataset) or out-of-distribution (train on one or more dataset(s) and test on a different dataset). Our already developed landmark-based technique, which is robust for in-distribution improvement against attacks in emotion categorization, could translate to out-of-distribution classification problems. This is important as different databases might have different variations such as in color or level of expressiveness of emotion. We compared the landmark-based method with four state-of-theart deep models (EfficientNetB0, InceptionV3, ResNet50 and VGG19), as well as emotion categorization tools (i.e. Py-Feat and the Azure Face API) by performing a cross-database experiment across six commonly used databases, i.e. CK+, JAFFE, KDEF, NIMH-ChEF, RAF, and PICS databases. The landmark-based method has achieved a significantly higher accuracy, achieving an average of 47.44% compared with most of the deep networks (< 36%) and the emotion categorization tools (<37%) with considerably less execution time. This highlights that out-ofdistribution emotion categorization is a much harder task due to detecting underlying emotional cues than emotion categorization in-distribution where superficial patterns are detected to > 97% accuracy.Impact Statement-Recognising emotions from people's faces has real-world applications for computer-based perception as it is often vital for interpersonal communication. Emotion recognition tasks nowadays are addressed using deep learning models that model colour distribution so classify images rather than emotion. This homogeneous knowledge representation is in contrast to emotion categorization, which is hypothesised as more heterogeneous landmark-based. This is investigated through out-of-distribution emotion categorization problems, where the test samples are drawn from a different dataset to training images. Our landmark-based method achieves a significantly higher classification performance (on average) compared with four state-of-the-art deep networks (EfficientNetB0, InceptionV3, ResNet50 and VGG19), as well as other emotion categorization This paper is an expanded paper published in the proceedings of the IEEE RO-MAN Conference, which was held (in Naples, Italy) virtually as live interactive conference between 31 August -

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Facial Expression Recognition Based on PCD-CNN with Pose and Expression

Dong

2020

Communications in Computer and Information Science

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Deep Explanation Model for Facial Expression Recognition Through Facial Action Coding Unit

Cited by 11 publications

References 14 publications

Deep learning for facial emotion recognition using custom CNN architecture

Deep learning for facial emotion recognition using custom CNN architecture

An Out-of-Distribution Attack Resistance Approach to Emotion Categorization

Facial Expression Recognition Based on PCD-CNN with Pose and Expression

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