ARFace: Attention-Aware and Regularization for Face Recognition With Reinforcement Learning

Zhang, Liping; Sun, Linjun; Yu, Lina; Dong, Xianlin; Chen, Jinchao; Cai, Weiwei; Wang, Chen; Ning, Xin

doi:10.1109/tbiom.2021.3104014

Cited by 45 publications

(12 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An agent is an entity that can perceive its environment through sensors and act upon that environment through actuators. The union of deep learning and reinforcement learning is effectively applied in deep FR such as attention-aware [82] and margin-aware [15] methods.…”

Section: Deep Reinforcement Learningmentioning

confidence: 99%

“…MaskTheFace [31] MaskedFace-Net [32], DCNN [33], CYCLE-GAN [34], IAMGAN [35], starGAN [36], segments [39][40][41], regularization [42], sparse rep. [43] Domain-specific models FaceNet [83], SphereFace [8], MFCosface [85], VGGFace [48], DeepID [86], LSTM-autoencoders [70], DC-SSDA [71], de-corrupt autoencoders [72], 3D autoencoder [73], pose invariant FR [77], makeup-invariant [78], DBNs [79,80], attention-aware [82], margin-aware [15] Feature extraction LBPs [44], SIFT [45], HOG [89], codebooks [90], multi-stage mask learning strategy [92], attention-aware and context-aware [93][94][95], GCN [96][97][98] Mask detection R-CNN [101], Fast R-CNN [102], Faster R-CNN [103], context-attention R-CNN [104], FCN [105], U-Net [106], FAN [109], LLE-CNNs [110], ...…”

Section: Conflicts Of Interestmentioning

confidence: 99%

See 1 more Smart Citation

Masked Face Recognition Using Deep Learning: A Review

et al. 2021

View full text Add to dashboard Cite

A large number of intelligent models for masked face recognition (MFR) has been recently presented and applied in various fields, such as masked face tracking for people safety or secure authentication. Exceptional hazards such as pandemics and frauds have noticeably accelerated the abundance of relevant algorithm creation and sharing, which has introduced new challenges. Therefore, recognizing and authenticating people wearing masks will be a long-established research area, and more efficient methods are needed for real-time MFR. Machine learning has made progress in MFR and has significantly facilitated the intelligent process of detecting and authenticating persons with occluded faces. This survey organizes and reviews the recent works developed for MFR based on deep learning techniques, providing insights and thorough discussion on the development pipeline of MFR systems. State-of-the-art techniques are introduced according to the characteristics of deep network architectures and deep feature extraction strategies. The common benchmarking datasets and evaluation metrics used in the field of MFR are also discussed. Many challenges and promising research directions are highlighted. This comprehensive study considers a wide variety of recent approaches and achievements, aiming to shape a global view of the field of MFR.

show abstract

Section: Deep Reinforcement Learningmentioning

confidence: 99%

Section: Conflicts Of Interestmentioning

confidence: 99%

Masked Face Recognition Using Deep Learning: A Review

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Facial expression recognition is a crucial part of human-computer interaction that allows computers to understand facial expressions based on human thinking. According to the processing of facial expression recognition process can be divided into three important face detection, feature extraction and classification module, face detection as the key technology of face recognition (Adjabi et al, 2020;Zhang et al, 2021) with its rapid development has basic mature, which can effectively extracted from the original face image of excellent characteristics and the characteristics of correct classification becomes key factor affecting the recognition result. For example, Gao and Ma (2020) obtained facial expression attributes from facial images so as to predict emotional states according to facial expression changes.…”

Section: Related Work Emotion Recognition Based On Facial Expressionsmentioning

confidence: 99%

“…We mainly convey personal emotions in three ways, namely language, voice and facial expressions. Scholars have found that facial expressions are the most important way of expressing human emotion information (Cai and Wei, 2020;Zhang et al, 2021). Facial expression information accounts for about 55 percent of the information transmitted by the experimenters, voice information for 38 percent, and language information accounts for only 7% of the total information.…”

Section: Introductionmentioning

confidence: 99%

Facial Expression Emotion Recognition Model Integrating Philosophy and Machine Learning Theory

Song

2021

Front. Psychol.

View full text Add to dashboard Cite

Facial expression emotion recognition is an intuitive reflection of a person’s mental state, which contains rich emotional information, and is one of the most important forms of interpersonal communication. It can be used in various fields, including psychology. As a celebrity in ancient China, Zeng Guofan’s wisdom involves facial emotion recognition techniques. His book Bing Jian summarizes eight methods on how to identify people, especially how to choose the right one, which means “look at the eyes and nose for evil and righteousness, the lips for truth and falsehood; the temperament for success and fame, the spirit for wealth and fortune; the fingers and claws for ideas, the hamstrings for setback; if you want to know his consecution, you can focus on what he has said.” It is said that a person’s personality, mind, goodness, and badness can be showed by his face. However, due to the complexity and variability of human facial expression emotion features, traditional facial expression emotion recognition technology has the disadvantages of insufficient feature extraction and susceptibility to external environmental influences. Therefore, this article proposes a novel feature fusion dual-channel expression recognition algorithm based on machine learning theory and philosophical thinking. Specifically, the feature extracted using convolutional neural network (CNN) ignores the problem of subtle changes in facial expressions. The first path of the proposed algorithm takes the Gabor feature of the ROI area as input. In order to make full use of the detailed features of the active facial expression emotion area, first segment the active facial expression emotion area from the original face image, and use the Gabor transform to extract the emotion features of the area. Focus on the detailed description of the local area. The second path proposes an efficient channel attention network based on depth separable convolution to improve linear bottleneck structure, reduce network complexity, and prevent overfitting by designing an efficient attention module that combines the depth of the feature map with spatial information. It focuses more on extracting important features, improves emotion recognition accuracy, and outperforms the competition on the FER2013 dataset.

show abstract

“…In recent years, facial emotion recognition based on deep learning technology (Cai and Wei, 2020;Cai et al, 2021;Zhang et al, 2021) has made great progress, but there are still many problems to be solved. For example, the recognition accuracy in real scenes is still not ideal.…”

Section: Introductionmentioning

confidence: 99%

MIFAD-Net: Multi-Layer Interactive Feature Fusion Network With Angular Distance Loss for Face Emotion Recognition

2021

Self Cite

View full text Add to dashboard Cite

Understanding human emotions and psychology is a critical step toward realizing artificial intelligence, and correct recognition of facial expressions is essential for judging emotions. However, the differences caused by changes in facial expression are very subtle, and different expression features are less distinguishable, making it difficult for computers to recognize human facial emotions accurately. Therefore, this paper proposes a novel multi-layer interactive feature fusion network model with angular distance loss. To begin, a multi-layer and multi-scale module is designed to extract global and local features of facial emotions in order to capture part of the feature relationships between different scales, thereby improving the model's ability to discriminate subtle features of facial emotions. Second, a hierarchical interactive feature fusion module is designed to address the issue of loss of useful feature information caused by layer-by-layer convolution and pooling of convolutional neural networks. In addition, the attention mechanism is also used between convolutional layers at different levels. Improve the neural network's discriminative ability by increasing the saliency of information about different features on the layers and suppressing irrelevant information. Finally, we use the angular distance loss function to improve the proposed model's inter-class feature separation and intra-class feature clustering capabilities, addressing the issues of large intra-class differences and high inter-class similarity in facial emotion recognition. We conducted comparison and ablation experiments on the FER2013 dataset. The results illustrate that the performance of the proposed MIFAD-Net is 1.02–4.53% better than the compared methods, and it has strong competitiveness.

show abstract

ARFace: Attention-Aware and Regularization for Face Recognition With Reinforcement Learning

Cited by 45 publications

References 57 publications

Masked Face Recognition Using Deep Learning: A Review

Masked Face Recognition Using Deep Learning: A Review

Facial Expression Emotion Recognition Model Integrating Philosophy and Machine Learning Theory

MIFAD-Net: Multi-Layer Interactive Feature Fusion Network With Angular Distance Loss for Face Emotion Recognition

Contact Info

Product

Resources

About