Facial expression recognition using feature additive pooling and progressive fine‐tuning of CNN

Kim, T.H.; Yu, Chanki; Lee, S.W.

doi:10.1049/el.2018.6932

Cited by 17 publications

(10 citation statements)

References 7 publications

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“…After determining the grading, these grades are assigned a score. Five grades are assigned a score by 10 points, namely, excellent grade score interval [8,10], good grade score interval [6,8], qualified grade score interval [4,6], basic qualified grade score interval [2,4], and unqualified grade score interval [0, 2]. Its grade evaluation function can be expressed as follows:…”

Section: E Simulation Process Of English Text and English Featurementioning

confidence: 99%

“…According to the differences in text information, scholars put forward targeted improvement strategies for the recognition of different text information [7]. Some scholars improve the information input mode based on the existing English text evaluation model and propose an English text analysis model based on neural network algorithm [8]. e data signals of the English feature recognition model are collected by the normalization method and are normalized by a neural network algorithm.…”

Section: Introductionmentioning

confidence: 99%

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[Retracted] Feature Recognition of English Based on Deep Belief Neural Network and Big Data Analysis

Liu

2021

Computational Intelligence and Neuroscience

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Realizing accurate recognition of Chinese and English information is a major difficulty in English feature recognition. Based on this difficulty, this paper studies the English feature recognition model based on deep belief network classification algorithm and Big Data analysis. First, the basic framework based on deep belief network classification algorithm and Big Data analysis is proposed. Combined with the Big Data analysis training model, the English feature information is processed. Through the recognition of different English text features, the recognition and matching of English features are realized. Then the errors of deep belief network classification algorithm and Big Data analysis are evaluated. Second, this paper describes the quantitative evaluation of deep belief network classification algorithm and Big Data analysis in this system. In the evaluation, the language feature evaluation method is used to improve the evaluation function. At the same time, the deep belief network classification algorithm and Big Data analysis are used to self-study the model, and the English feature recognition method with strong applicability is established. Finally, the effectiveness of the recognition system is verified by the experiment.

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Section: E Simulation Process Of English Text and English Featurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

[Retracted] Feature Recognition of English Based on Deep Belief Neural Network and Big Data Analysis

Liu

2021

Computational Intelligence and Neuroscience

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“…Emotion generation is an important social tool for human communication and social interaction. In human interaction, external emotions and internal emotions are also reflected in facial and physiological reactions, which are directed by the brain, and then, affect human facial expressions, intonation, heartbeat, and blood pressure 16‐18 . As the internal state is related to growth experience and innate environmental impact, under the same external changes, various people will have different emotions.…”

Section: Related Workmentioning

confidence: 99%

Data fusion analysis for attention‐deficit hyperactivity disorder emotion recognition with thermal image and Internet of Things devices

et al. 2020

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Summary Attention‐deficit hyperactivity disorder (ADHD) is a symptom of behavioral or emotional problems as these problems affect children's learning and social integration. With the advancements in the Internet of Things (IoTs), emotions can be detected through image and physiological data. However, some critical ADHD children are often accompanied by the inability to control their body and even facial expressions, making emotion recognition technologies difficult to develop successfully. This study aims to predict the emotions of ADHD children and to address their emotional problems with related IoT robotic devices. Data fusion analysis technology for facial expressions was used to combine thermal images and recognition data, while deep reinforcement learning technology was used to periodically stream information for ADHD students, in alignment with intervention strategies that were designed to address behavioral problems.

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“…With the advent of the big data era , deep learning methods, such as artificial neural networks, have emerged. For example, Kim et al [49] proposed a CNN method for FER to avoid the complex feature extraction process in traditional FER. They extracted the hidden features of the expression images by training the convolution kernels, and used maximum pooling to reduce the dimensions of the extracted features.…”

Section: B Facial Feature Extraction and Recognitionmentioning

confidence: 99%

WGAN-Based Robust Occluded Facial Expression Recognition

et al. 2019

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Research on facial expression recognition (FER) technology can promote the development of theoretical and practical applications for our daily life. Currently, most of the related works on this technology are focused on un-occluded FER. However, in real life, facial expression images often have partial occlusion; therefore, the accurate recognition of occluded facial expression images is a topic that should be explored. In this paper, we proposed a novel Wasserstein generative adversarial network-based method to perform occluded FER. After complementing the face occlusion image with complex facial expression information, the recognition is achieved by learning the facial expression features of the images. This method consists of a generator G and two discriminators D 1 and D 2. The generator naturally complements occlusion in the expression image under the triple constraints of weighted reconstruction loss l wr , triplet loss l t , and adversarial loss l a. We optimize the discriminator D 1 to distinguish between real and fake by constructing an adversarial loss l a between the generated complementing images, original un-occluded images, and smallscale-occluded images based on the Wasserstein distance. Finally, the FER is completed by introducing classification loss l c into D 2. To verify the effectiveness of the proposed method, an experimental analysis was performed on the AffectNet and RAF-DB datasets. The visual occlusion complementing results, comparison of recognition rates of facial expression images with and without de-occlusion processing, and T-distributed stochastic neighbor embedding visual analysis of facial expression features all prove the effectiveness of the proposed method. The experimental results show that the proposed method is better than the existing state-of-the-art methods. INDEX TERMS Facial expression recognition, partial occlusion, image complementation, Wasserstein generative adversarial network.

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Facial expression recognition using feature additive pooling and progressive fine‐tuning of CNN

Cited by 17 publications

References 7 publications

[Retracted] Feature Recognition of English Based on Deep Belief Neural Network and Big Data Analysis

[Retracted] Feature Recognition of English Based on Deep Belief Neural Network and Big Data Analysis

Data fusion analysis for attention‐deficit hyperactivity disorder emotion recognition with thermal image and Internet of Things devices

WGAN-Based Robust Occluded Facial Expression Recognition

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