RETRACTED ARTICLE: Residual attention network for deep face recognition using micro-expression image analysis

Chinnappa, Gnanaprakasam; Rajagopal, Manoj Kumar

doi:10.1007/s12652-021-03003-4

Cited by 8 publications

(8 citation statements)

References 23 publications

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“…Multiple MER works [84], [99], [156], [157], [158] employed residual blocks for robust recognition on smallscale ME datasets. Instead of directly applying the shortcut connection, [159] further designed a convolutionable shortcut to learn the important residual information and AffectiveNet [160] introduced an MFL module learning the low-and high-level feature parallelly to increase the discriminative capability between the inter and intra-class variations.…”

Section: Network Blockmentioning

confidence: 99%

Deep Learning for Micro-Expression Recognition: A Survey

Wei

Liu

et al. 2022

IEEE Trans. Affective Comput.

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Micro-expressions (MEs) are involuntary facial movements revealing people's hidden feelings in high-stake situations and have practical importance in various fields. Early methods for Micro-expression Recognition (MER) are mainly based on traditional features. Recently, with the success of Deep Learning (DL) in various tasks, neural networks have received increasing interest in MER. Different from macro-expressions, MEs are spontaneous, subtle, and rapid facial movements, leading to difficult data collection and annotation, thus publicly available datasets are usually small-scale. Currently, various DL approaches have been proposed to solve the ME issues and improve MER performance. In this survey, we provide a comprehensive review of deep MER and define a new taxonomy for the field encompassing all aspects of MER based on DL, including datasets, each step of the deep MER pipeline, and performance comparisons of the most influential methods. The basic approaches and advanced developments are summarized and discussed for each aspect. Additionally, we conclude the remaining challenges and potential directions for the design of robust MER systems. Finally, ethical considerations in MER are discussed. To the best of our knowledge, this is the first survey of deep MER methods, and this survey can serve as a reference point for future MER research.

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Section: Network Blockmentioning

confidence: 99%

Deep Learning for Micro-Expression Recognition: A Survey

Wei

Liu

et al. 2022

IEEE Trans. Affective Comput.

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“…Therefore, the attention mechanism has been widely used in isomorphic facial expression recognition. Chinnappa et al [ 52 ] proposed focusing attention on the microexpression analysis, fusing the feature information of channel and space dimensions, to obtain the interspatial kindred matrix. Wang et al [ 53 ] proposed a new anchor-level attention, highlighting the features from the face region.…”

Section: Related Workmentioning

confidence: 99%

Image-Based Pain Intensity Estimation Using Parallel CNNs with Regional Attention

Liang

et al. 2022

Bioengineering

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Automatic pain estimation plays an important role in the field of medicine and health. In the previous studies, most of the entire image frame was directly imported into the model. This operation can allow background differences to negatively affect the experimental results. To tackle this issue, we propose the parallel CNNs framework with regional attention for automatic pain intensity estimation at the frame level. This modified convolution neural network structure combines BlurPool methods to enhance translation invariance in network learning. The improved networks can focus on learning core regions while supplementing global information, thereby obtaining parallel feature information. The core regions are mainly based on the tradeoff between the weights of the channel attention modules and the spatial attention modules. Meanwhile, the background information of the non-core regions is shielded by the DropBlock algorithm. These steps enable the model to learn facial pain features adaptively, not limited to a single image pattern. The experimental result of our proposed model outperforms many state-of-the-art methods on the RMSE and PCC metrics when evaluated on the diverse pain levels of over 12,000 images provided by the publicly available UNBC dataset. The model accuracy rate has reached 95.11%. The experimental results show that the proposed method is highly efficient at extracting the facial features of pain and predicts pain levels with high accuracy.

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“…al [179] introduced multi-scale shortcut connections into pre-trained deep networks to solve the gradient disappearance problem. Instead directly applying the shortcut connection, [180] designed a convolutionable shortcut to learn the important residual information. Furthermore, inspiblack by the residual concept, AffectiveNet introduced a MFL module learning the low-level and high-level feature parallelly to increase the discriminable capability between the inter and intra-class variations.…”

Section: A Network Blockmentioning

confidence: 99%

Deep Learning for Micro-expression Recognition: A Survey

Li¹,

Wei²,

Liu³

et al. 2021

Preprint

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Micro-expressions (MEs) are involuntary facial movements revealing people's hidden feelings in high-stake situations and have practical importance in medical treatment, national security, interrogations and many human-computer interaction systems. Early methods for MER mainly based on traditional appearance and geometry features. Recently, with the success of deep learning (DL) in various fields, neural networks have received increasing interests in MER. Different from macro-expressions, MEs are spontaneous, subtle, and rapid facial movements, leading to difficult data collection, thus have small-scale datasets. DL based MER becomes challenging due to above ME characters. To data, various DL approaches have been proposed to solve the ME issues and improve MER performance. In this survey, we provide a comprehensive review of deep microexpression recognition (MER), including datasets, deep MER pipeline, and the bench-marking of most influential methods. This survey defines a new taxonomy for the field, encompassing all aspects of MER based on DL. For each aspect, the basic approaches and advanced developments are summarized and discussed. In addition, we conclude the remaining challenges and and potential directions for the design of robust deep MER systems. To the best of our knowledge, this is the first survey of deep MER methods, and this survey can serve as a reference point for future MER research.

show abstract

RETRACTED ARTICLE: Residual attention network for deep face recognition using micro-expression image analysis

Cited by 8 publications

References 23 publications

Deep Learning for Micro-Expression Recognition: A Survey

Deep Learning for Micro-Expression Recognition: A Survey

Image-Based Pain Intensity Estimation Using Parallel CNNs with Regional Attention

Deep Learning for Micro-expression Recognition: A Survey

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