Video facial emotion recognition based on local enhanced motion history image and CNN-CTSLSTM networks

Hu, Min; Wang, Haowen; Wang, Xiaohua; Yang, Juan; Wang, Ronggui

doi:10.1016/j.jvcir.2018.12.039

Cited by 94 publications

(49 citation statements)

References 20 publications

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“…Typically, low and high-level feature extractors are combined in a base network architecture. We choose VGG16 [22] as the base network because this network is still used as the base of many recent network for the FER task [31], [32], [33]. From the base network, VGG16 [22], we separated into two parts for two levels of input.…”

Section: Pyramid With Super-resolution (Psr) Networkmentioning

confidence: 99%

Pyramid With Super Resolution for In-the-Wild Facial Expression Recognition

et al. 2020

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Facial Expression Recognition (FER) is a challenging task that improves natural humancomputer interaction. This paper focuses on automatic FER on a single in-the-wild (ITW) image. ITW images suffer real problems of pose, direction, and input resolution. In this study, we propose a pyramid with super-resolution (PSR) network architecture to solve the ITW FER task. We also introduce a prior distribution label smoothing (PDLS) loss function that applies the additional prior knowledge of the confusion about each expression in the FER task. Experiments on the three most popular ITW FER datasets showed that our approach outperforms all the state-of-the-art methods.

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Section: Pyramid With Super-resolution (Psr) Networkmentioning

confidence: 99%

Pyramid With Super Resolution for In-the-Wild Facial Expression Recognition

et al. 2020

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“…The convolutional layer and pooling layer are two critical layers of convolution neural network. In most cases, a convolutional layer is a useful tool for extracting sophisticated high-dimensional input features [34], and the pooling layer can reduce the space size of the input convolution layer and avoid overfitting of the network. CNN has a strong ability to extract features from the input, and it is widely applied in the field of image processing and text recognition [35][36].…”

Section: Architecture Of the Proposed Networkmentioning

confidence: 99%

A Reliable Prognosis Approach for Degradation Evaluation of Rolling Bearing Using MCLSTM

Huang

et al. 2020

Sensors

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Prognostics and health management technology (PHM), a measure to ensure the reliability and safety of the operation of industrial machinery, has attracted attention and application adequately. However, how to use the monitored information to evaluate the degradation of rolling bearings is a significant issue for its predictive maintenance and autonomic logistics. This work presents a reliable health prognosis approach to estimate the health indicator (HI) and remaining useful life (RUL) of rolling bearings. Firstly, to accurately capture the degradation process, a novel health index (HI) is constructed based on correlation kurtosis for different iteration periods and a Gaussian process latency variable model (GPLVM). Then, a multiple convolutional long short-term memory (MCLSTM) network is proposed to predict HI values and RUL values. Finally, we perform experimental datasets of rolling bearings, demonstrating that the presented method surpasses other state-of-the-art prognosis approaches. The results also confirm the feasibility of the presented method in industrial machinery.

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“…The face emotion-based interaction process has recently received much interest and attractiveness. The human face is a major part in processing the expression based on emotions, and also identification in social life [6]. Generally, humans keep on changing their emotions based on mental or physical instances occasionally.…”

Section: Introductionmentioning

confidence: 99%

Optimization Assisted Convolutional Neural Network for Facial Emotion Recognition

Sarkar¹

2020

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Facial Expression Recognition (FER) is an important type of visual information that can be used to understand a human"s emotional situation. FER has attained a significant interest in human-computer interaction, autopilot, medical healing as well as various face expression dependent areas, and it is enormously used in most research areas. Hence, this paper intends to develop an intelligent facial emotion recognition model by following two major processes namely (a) Feature extraction and (b) Classification. Initially, the input image is subjected to extract Local Binary Pattern (LBP) based features. Further, the extracted features are classified using a Convolutional neural network (CNN). Moreover, the weights of CNN are optimally tuned by the Improvised Steering angle and Gear-based ROA (ISG-ROA) algorithm. Finally, the superiority of the ISG-ROA method is compared over existing methods and its improvement is proved effective.

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Video facial emotion recognition based on local enhanced motion history image and CNN-CTSLSTM networks

Cited by 94 publications

References 20 publications

Pyramid With Super Resolution for In-the-Wild Facial Expression Recognition

Pyramid With Super Resolution for In-the-Wild Facial Expression Recognition

A Reliable Prognosis Approach for Degradation Evaluation of Rolling Bearing Using MCLSTM

Optimization Assisted Convolutional Neural Network for Facial Emotion Recognition

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