Attention Bilinear Pooling for Fine-Grained Facial Expression Recognition

Liu, Liyuan; Zhang, Lifeng; Jia, Shixiang

doi:10.1007/978-3-030-37352-8_47

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…Increased by 3.3 and 4.7%. Compared with the current mainstream methods on the FER-2013 data set, DNNRL ( Kim et al, 2016 ) proposed combining multiple CNNs and using weighted joint decision-making methods, and ICL ( Liu and Zhou, 2020 ) proposed clustering to obtain the center distance of expression classes and continuously Adding the method of difficult sample training and the method of combining bilinear pooling and attention mechanism proposed by ABP ( Liu et al, 2019 ), the method in this paper has achieved superior performance and achieved a higher recognition rate of 74.00%.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Facial Expression Emotion Recognition Model Integrating Philosophy and Machine Learning Theory

Song

2021

Front. Psychol.

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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.

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Section: Experiments and Resultsmentioning

confidence: 99%

Facial Expression Emotion Recognition Model Integrating Philosophy and Machine Learning Theory

Song

2021

Front. Psychol.

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“…Recent studies [38,39] show that bilinear pooling exhibits more expressive ability among feature fusion approaches by exploiting higherorder information. However, bilinear pooling generates redundant information in an exceptionally high-dimensional space [40], therefore suffering from the burstiness phenomenon [41]. Gao et al [42] proposed a kernelized bilinear pooling method to reduce the feature dimensionality.…”

Section: Feature Fusionmentioning

confidence: 99%

Synergy Factorized Bilinear Network with a Dual Suppression Strategy for Brain Tumor Classification in MRI

et al. 2021

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Automatic brain tumor classification is a practicable means of accelerating clinical diagnosis. Recently, deep convolutional neural network (CNN) training with MRI datasets has succeeded in computer-aided diagnostic (CAD) systems. To further improve the classification performance of CNNs, there is still a difficult path forward with regards to subtle discriminative details among brain tumors. We note that the existing methods heavily rely on data-driven convolutional models while overlooking what makes a class different from the others. Our study proposes to guide the network to find exact differences among similar tumor classes. We first present a “dual suppression encoding” block tailored to brain tumor MRIs, which diverges two paths from our network to refine global orderless information and local spatial representations. The aim is to use more valuable clues for correct classes by reducing the impact of negative global features and extending the attention of salient local parts. Then we introduce a “factorized bilinear encoding” layer for feature fusion. The aim is to generate compact and discriminative representations. Finally, the synergy between these two components forms a pipeline that learns in an end-to-end way. Extensive experiments exhibited superior classification performance in qualitative and quantitative evaluation on three datasets.

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“…Therefore, some methods perform bilinear pooling operation to get more powerful representation, which calculates the second-order statistics of local features that can obtain better feature representation. For example, Li et al [23] proposed fine-brunch and coarse-brunch to obtain different level bilinear features respectively, followed by softmax loss layers with semantic information from hierarchical labels. Although these methods can enhance the feature representation ability, and further improve the classification accuracy, while neglect the size of parameters.…”

Section: A Weakly-supervised Fine-grained Image Recognitionmentioning

confidence: 99%

Self-Layer and Cross-Layer Bilinear Aggregation for Fine-Grained Recognition in Cyber-Physical-Social Systems

et al. 2020

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Cyber-Physical-Social Systems (CPSS) integrates cyber, physical and social spaces together, which makes our lives more convenient and intelligent by providing personalized service. In this paper, we will provide CPSS service for fine-grained recognition. Fine-grained visual recognition is a hot but challenging research in computer vision that aims to recognize object subcategories. The reason why it is challenging is that it extremely depends on the subtle discriminative features of local parts. Recently, some bilinear feature based methods were proposed, and the experimental results show state-of-the-art performance. However, most of them neglect the spatial relationships of part-region feature among multiple layers. In this paper, a novel approach of Self-layer and Cross-layer Bilinear Aggregation(SCBA) is proposed for fine-grained recognition. Firstly, a self-layer bilinear feature fusion module is proposed to model the spatial relationship of feature at the same layer. Secondly, we propose a cross-layer bilinear feature fusion module to capture the inter-layer interreaction of information to boost the ability of feature representation. In summary, the method we proposed not only can learn the correlations among different layers but the same layer, which makes it efficient and the experimental results show that it achieves state-of-the-art accuracy on three common fine-grained image datasets.

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Attention Bilinear Pooling for Fine-Grained Facial Expression Recognition

Cited by 3 publications

References 9 publications

Facial Expression Emotion Recognition Model Integrating Philosophy and Machine Learning Theory

Facial Expression Emotion Recognition Model Integrating Philosophy and Machine Learning Theory

Synergy Factorized Bilinear Network with a Dual Suppression Strategy for Brain Tumor Classification in MRI

Self-Layer and Cross-Layer Bilinear Aggregation for Fine-Grained Recognition in Cyber-Physical-Social Systems

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