Gait recognition based on capsule network

Xu, Zhaopeng; Lu, Wei; Zhang, Qin; Yeung, Yuileong; Chen, Xin

doi:10.1016/j.jvcir.2019.01.023

Cited by 40 publications

(26 citation statements)

References 17 publications

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“…(4) , a ij donates the log probability. The sum of the correlation coefficients between capsule i , and capsules in the top layer is 1 and the log prior probability is determined by Softmax [47] . In capsule networks, a margin loss has been proposed to determine whether objects of a particular class are present and can be calculated with the Eq.…”

Section: Methodsmentioning

confidence: 99%

“…The value of T k is 1 if and only if the class k is present. m + = 0.9 ve m − = 0.1 are the hyper parameters and denotes down-weighting of the loss [47] . The length of the vectors calculated in the capsule networks indicates the probability of being in that part of the image, while the direction of the vector contains the parameter information such as texture, color, position, size, etc.…”

Section: Methodsmentioning

confidence: 99%

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Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks

Toraman

Alakuş

Türkoğlu

2020

Chaos, Solitons & Fractals

286

183

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Highlights In this study, unlike CNN architectures, COVID-19 was determined from chest X-ray images with a smaller number of layers. More COVID-19, pneumonia, and no-findings images were used than in previous studies. This increases the reliability of the system more. As is known, reducing the size of the image may cause some information in the image to be lost. Given these facts, good classification accuracy has been achieved with capsule networks, even the image size has been reduced to 128 × 128 pixels.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks

Toraman

Alakuş

Türkoğlu

2020

Chaos, Solitons & Fractals

286

183

View full text Add to dashboard Cite

show abstract

“…The capsule neural network can capture relative relationship between features of different local regions and represent features more effectively, and it may be beneficial to some similar image based recognition task, like face recognition [44] and gait recognition [45]. So our proposed E2‐Capsnet using AU‐aware attention is likely to be effective for face recognition and facial AU detection.…”

Section: Discussionmentioning

confidence: 99%

E2‐capsule neural networks for facial expression recognition using AU‐aware attention

Cao

Yao

2020

IET image process

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Capsule neural network is a new and popular technique in deep learning. However, the traditional capsule neural network does not extract features sufficiently before the dynamic routing between the capsules. In this paper, the one Double Enhanced Capsule Neural Network (E2-Capsnet) that uses AU-aware attention for facial expression recognition (FER) is proposed. The E2-Capsnet takes advantage of dynamic routing between the capsules, and has two enhancement modules which are beneficial for FER. The first enhancement module is the convolutional neural network with AU-aware attention, which can help focus on the active areas of the expression. The second enhancement module is the capsule neural network with multiple convolutional layers, which enhances the ability of the feature representation. Finally, squashing function is used to classify the facial expression. We demonstrate the effectiveness of E2-Capsnet on the two public benchmark datasets, RAF-DB and EmotioNet. The experimental results show that our E2-Capsnet is superior to the state-of-the-art methods. Our implementation will be publicly available online.

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“…Zhu et al [16] use it to diagnosis bearing fault for rotating machine health monitoring. Xu et al [22] find an effective model based on capsule network to capture more discriminative features and promote gait recognition performance. Wang et al [23] explore a Capsule network for protein post-translational modification site prediction.…”

Section: Related Workmentioning

confidence: 99%

Cv-CapsNet: Complex-Valued Capsule Network

Cheng

2019

IEEE Access

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Capsule network (CapsNet) can recognize the objects by encoding the part-whole relationships in a way similar to our human perceptual system and has already shown its great potential in image classification tasks. However, it is limited to the real domain while the complex numbers having much richer representational capacity and facilitating the noise-robust memory retrieval mechanisms. Therefore, we propose two architectures: Complex-valued Dense CapsNet (Cv-CapsNet) and Complex-valued Diverse CapsNet (Cv-CapsNet++), each of them consists of three stages. In the first stage, multi-scale complex-valued features are obtained by the restricted dense complex-valued subnetwork. Particularly, Cv-CapsNet++ utilizes a three-level Cv-CapsNet hierarchical model to extract the multi-scale high-level complex-valued features in order to adapt to the complicated datasets. In the second stage, these complexvalued features are encoded into the complex-valued primary capsules, Particularly, Cv-CapsNet++ encodes the complex-valued features from different hierarchies into the multi-dimensional complex-valued primary capsules. In the third stage, we generalize the dynamic routing algorithm to the complex-valued domain and employ it to fuse the real-and imaginary-valued information of complex-valued primary capsules. The experimental results show that the proposed architectures lead to fewer trainable parameters, better performance, and fewer iterations during training than Real-valued CapsNets (Rv-CapsNets) with similar structure and original CapsNet on FashionMNIST and CIFAR10 datasets. INDEX TERMS Capsule network, complex-valued capsule network, CNNs, deep learning.

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Gait recognition based on capsule network

Cited by 40 publications

References 17 publications

Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks

Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks

E2‐capsule neural networks for facial expression recognition using AU‐aware attention

Cv-CapsNet: Complex-Valued Capsule Network

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