Triplet Loss With Multistage Outlier Suppression and Class-Pair Margins for Facial Expression Recognition

Xie, Weicheng; Wu, Haoqian; Tian, Youliang; Bai, Mengchao; Shen, Linlin

doi:10.1109/tcsvt.2021.3063052

Cited by 32 publications

(7 citation statements)

References 54 publications

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“…where d(i, label) and d(i, i) are the Euclidean distances from candidate indicator i to object label and another indicator i, and ℓ is the minimum distance between these two distances. Empirically speaking, ℓ is usually set to 0.6 [39], and d( * , * ) uses the softmax function to convert its value between 0 and 1. However, this distance is based on the solution of the spatial distance.…”

Section: Feature Generationmentioning

confidence: 99%

The Evaluation of DDoS Attack Effect Based on Neural Network

Guo¹,

Qiu²,

Liu³

et al. 2022

Security and Communication Networks

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DDoS attack effect evaluation is the basis of security strategy deployment. The traditional effect evaluation method relies on the original data, ignoring the relationship between features and the evaluation target and indicator data redundancy, which affects the accuracy and reliability of the evaluation result. To this end, we introduce distance entropy to measure the similarity between features and evaluation target and use LSTM and Triplet networks to measure multiple correlations simultaneously. Then, a 2D-CNN is used to mine deep feature information and filter irrelevant information. We also combine 1D-CNN and attention models to achieve hierarchical sampling of different local features. Finally, three fully connected layers’ training obtains a total evaluation value. We conducted experiments on five commonly used DDoS datasets. The results showed that the average ranking accuracy of the neural network-based DDoS attack evaluation method (NNDE) reached 87.2%, 91.3%, 88%, 85.6%, and 94.5%, respectively. Compared with other evaluation methods, an average increase of 19.73% indicates that this method can better evaluate the effect of DDoS attacks.

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Section: Feature Generationmentioning

confidence: 99%

The Evaluation of DDoS Attack Effect Based on Neural Network

Guo¹,

Qiu²,

Liu³

et al. 2022

Security and Communication Networks

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“…Triplet loss enhances both inter-class compactness and intra-class separability. The authors in [5] developed a classpaired margin identifier mechanism in which the association with an outlier detector improves the performance of FER application. They proposed an outlier detector preventing occluded and confusing samples from having contributions in selected negative samples.…”

Section: A Triplet Loss Sampling Strategies In Classificationmentioning

confidence: 99%

Triplet Loss-less Center Loss Sampling Strategies in Facial Expression Recognition Scenarios

Hossein¹,

Lotfi²,

Atyabi³

et al. 2023

Preprint

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Facial expressions convey massive information and play a crucial role in emotional expression. Deep neural network (DNN) accompanied by deep metric learning (DML) techniques boost the discriminative ability of the model in facial expression recognition (FER) applications. DNN, equipped with only classification loss functions such as Cross-Entropy cannot compact intra-class feature variation or separate inter-class feature distance as well as when it gets fortified by a DML supporting loss item. The triplet center loss (TCL) function is applied on all dimensions of the sample's embedding in the embedding space. In our work, we developed three strategies: fully-synthesized, semi-synthesized, and prediction-based negative sample selection strategies. To achieve better results, we introduce a selective attention module that provides a combination of pixel-wise and element-wise attention coefficients using high-semantic deep features of input samples. We evaluated the proposed method on the RAF-DB, a highly imbalanced dataset. The experimental results reveal significant improvements in comparison to the baseline for all three negative sample selection strategies.

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“…Holistic-based methods treat the face as a whole and typically solves occlusion and variant-pose issues based on feature reconstruction of geometry [36] texture [37] or improvement of loss function [12], [38] and synthesis of facial expression [14], [15]. Zhang et al [36] combined the iterative closest point (ICP) algorithm and fuzzy C-means to construct a facial point detector and reconstructed 54 facial points of occluded and variant poses.…”

Section: B Computer Vision Fermentioning

confidence: 99%

“…Zhang et al [36] combined the iterative closest point (ICP) algorithm and fuzzy C-means to construct a facial point detector and reconstructed 54 facial points of occluded and variant poses. Xie et al [12] proposed a new triplet loss based on class-pair margins and multistage outlier suppression to enhance interclass separability and intraclass compactness of network features. Zhang et al [14] performed facial expression recognition and facial image synthesis simultaneously based on a generative adversarial network (GAN) to ease the overfitting problem in the FER task.…”

Section: B Computer Vision Fermentioning

confidence: 99%

“…Although researchers are working to increase the diversity of real-world datasets [9], [10] to improve the versatility of the model, occlusion and variant poses markedly change facial visual appearances, resulting in inaccurate feature location, imprecise face alignment or inefficient feature extraction, which make FER still a challenging. Traditional methods consider a face as a whole [11] and solve the FER problem by optimising a loss function [12], [13] or synthesizing facial expressions [14], [15] to improve generalizability. However, these methods pay less attention to the potentially diverse emotional information provided by facial details, and irregular faces caused by occlusion and variant poses also strongly affect the model's ability to extract features.…”

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confidence: 99%

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Adaptive Multilayer Perceptual Attention Network for Facial Expression Recognition

Liu

Cai

Lin

et al. 2022

IEEE Trans. Circuits Syst. Video Technol.

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In complex real-world situations, problems such as illumination changes, facial occlusion, and variant poses make facial expression recognition (FER) a challenging task. To solve the robustness problem, this paper proposes an adaptive multilayer perceptual attention network (AMP-Net) that is inspired by the facial attributes and the facial perception mechanism of the human visual system. AMP-Net extracts global, local, and salient facial emotional features with different fine-grained features to learn the underlying diversity and key information of facial emotions. Different from existing methods, AMP-Net can adaptively guide the network to focus on multiple finer and distinguishable local patches with robustness to occlusion and variant poses, improving the effectiveness of learning potential facial diversity information. In addition, the proposed global perception module can learn different receptive field features in the global perception domain, and AMP-Net also supplements salient facial region features with high emotion correlation based on prior knowledge to capture key texture details and avoid important information loss. Many experiments show that AMP-Net achieves good generalizability and state-of-the-art results on several real-world datasets, including RAF-DB, AffectNet-7, AffectNet-8, SFEW 2.0, FER-2013, and FED-RO, with accuracies of 89.25%, 64.54%, 61.74%, 61.17%, 74.48%, and 71.75%, respectively. All codes and training logs are publicly available at https://github.com/liuhw01/AMP-Net.

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Triplet Loss With Multistage Outlier Suppression and Class-Pair Margins for Facial Expression Recognition

Cited by 32 publications

References 54 publications

The Evaluation of DDoS Attack Effect Based on Neural Network

The Evaluation of DDoS Attack Effect Based on Neural Network

Triplet Loss-less Center Loss Sampling Strategies in Facial Expression Recognition Scenarios

Adaptive Multilayer Perceptual Attention Network for Facial Expression Recognition

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