Dynamic Facial Expression Recognition Under Partial Occlusion With Optical Flow Reconstruction

Poux, Delphine; Allaert, Benjamin; Ihaddadene, Nacim; Bilasco, Ioan Marius; Djeraba, Chabane; Bennamoun, Mohammed

doi:10.1109/tip.2021.3129120

Cited by 36 publications

(6 citation statements)

References 27 publications

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“…Convolutional neural network firstly perceives local features, and then integrates these local features at a high level to obtain the global features and topological structure of the image, and then judge the attributes and categories of the image [33,34]. Therefore, convolutional neural networks are highly invariant to shape translation, scale scaling, tilt or other forms of deformation.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

A novel art gesture recognition model based on two channel region-based convolution neural network for explainable human-computer interaction understanding

Zhao

2022

COMSIS J

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The application development of hot technology is both an opportunity and a challenge. The vision-based gesture recognition rate is low and real-time performance is poor, so various algorithms need to be studied to improve the accuracy and speed of recognition. In this paper, we propose a novel gesture recognition based on two channel region-based convolution neural network for explainable human-computer interaction understanding. The input gesture image is extracted through two mutually independent channels. The two channels have convolution kernel with different scales, which can extract the features of different scales in the input image, and then carry out feature fusion at the fully connection layer. Finally, it is classified by the softmax classifier. The two-channel convolutional neural network model is proposed to solve the problem of insufficient feature extraction by the convolution kernel. Experimental results of gesture recognition on public data sets NTU and VIVA show that the proposed algorithm can effectively avoid the over-fitting problem of training models, and has higher recognition accuracy and stronger robustness than traditional algorithms.

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Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

A novel art gesture recognition model based on two channel region-based convolution neural network for explainable human-computer interaction understanding

Zhao

2022

COMSIS J

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“…Similarly, Poux et al investigated facial expression recognition in the presence of partial occlusion [11]. They proposed a new method aiming to reconstruct the occluded part of the face for FER.…”

Section: Related Workmentioning

confidence: 99%

Comparing the Robustness of Humans and Deep Neural Networks on Facial Expression Recognition

et al. 2022

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Emotion recognition, and more particularly facial expression recognition (FER), has been extensively used for various applications (e.g., human–computer interactions). The ability to automatically recognize facial expressions has been facilitated with recent progress in the fields of computer vision and artificial intelligence. Nonetheless, FER algorithms still seem to face difficulties with image degradations due to real-life conditions (e.g., because of image compression or transmission). In this paper, we propose to investigate the impact of different distortion configurations on a large number of images of faces on human performance, thanks to the conduct of a crowdsourcing experiment. We further compare human performance with two open-source FER algorithms. Results show that, overall, models are more sensitive to distortions than humans—even when fine-tuned. Furthermore, we broach the subject of annotation errors and bias which exist in several well-established datasets, and suggest approaches to improve the latter.

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“…Deep face recognition models have delivered impressive performance on public benchmarks (Wen et al, 2016;Cao et al, 2018;Deng et al, 2019) and realistic scenarios (Anwar & Raychowdhury, 2020). In general, these models are designed for recognizing unmasked faces and often suffer from sharp accuracy drop in recognizing masked faces (Ngan et al, 2020a;, which hinders real-world applications (Ge et al, 2017;Poux et al, 2022;Zhang et al, 2023;Wang et al, 2023;Al-Nabulsi et al, 2023). Unlike normal face recognition, masked face recognition is challenged by insufficient or inaccurate representations.…”

Section: Introductionmentioning

confidence: 99%

Masked face recognition with convolutional visual self-attention network

Liu

et al. 2023

Neurocomputing

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Dynamic Facial Expression Recognition Under Partial Occlusion With Optical Flow Reconstruction

Cited by 36 publications

References 27 publications

A novel art gesture recognition model based on two channel region-based convolution neural network for explainable human-computer interaction understanding

A novel art gesture recognition model based on two channel region-based convolution neural network for explainable human-computer interaction understanding

Comparing the Robustness of Humans and Deep Neural Networks on Facial Expression Recognition

Masked face recognition with convolutional visual self-attention network

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