Accurate and Reliable Facial Expression Recognition Using Advanced Softmax Loss With Fixed Weights

Jiang, Ping; Liu, Gang; Wang, Quan; Wu, Jiang

doi:10.1109/lsp.2020.2989670

Cited by 21 publications

(18 citation statements)

References 26 publications

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“…The two researchers listed above use the Softmax function for the CNN parameters and the Adam optimization algorithm. According to the test findings, the Adam Optimization technique proposed by [36] comparing to the softmax function was used by [60], accomplishing a higher precision score of 87 percent accuracy. It is observed that everyone reached good results, especially in terms of accuracy, with different rates, and using different data sets, which impacts the results.…”

Section: Discussionmentioning

confidence: 99%

“…Jiang et al [60] introduced a new loss feature called the advanced softmax loss to eradicate imbalanced training expressions. The proposed losses guarantee that any class would have a level playing field and potential using fixed (unlearnable) weight parameters of the same size and equally allocated in angular space.…”

Section: Facial Expression Recognition Surveymentioning

confidence: 99%

See 1 more Smart Citation

Facial Expression Recognition Based on Deep Learning Convolution Neural Network: A Review

Abdullah

Abdulazeez

2021

JSCDM

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Facial emotional processing is one of the most important activities in effective calculations, engagement with people and computers, machine vision, video game testing, and consumer research. Facial expressions are a form of nonverbal communication, as they reveal a person's inner feelings and emotions. Extensive attention to Facial Expression Recognition (FER) has recently been received as facial expressions are considered. As the fastest communication medium of any kind of information. Facial expression recognition gives a better understanding of a person's thoughts or views and analyzes them with the currently trending deep learning methods. Accuracy rate sharply compared to traditional state-of-the-art systems. This article provides a brief overview of the different FER fields of application and publicly accessible databases used in FER and studies the latest and current reviews in FER using Convolution Neural Network (CNN) algorithms. Finally, it is observed that everyone reached good results, especially in terms of accuracy, with different rates, and using different data sets, which impacts the results.

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Section: Discussionmentioning

confidence: 99%

Section: Facial Expression Recognition Surveymentioning

confidence: 99%

Facial Expression Recognition Based on Deep Learning Convolution Neural Network: A Review

Abdullah

Abdulazeez

2021

JSCDM

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“…It is evident from the Table 7 that the proposed technique has outperformed all the existing techniques. Li et al [15] 75.8 Lee et al [25] 6 5 Wang et al [27] 73.75 Jiang et al [28] 70.8 Hussein et al [29] 8 1…”

Section: Comparison With the State-of-the-art Techniquesmentioning

confidence: 99%

A Novel Efficient Patient Monitoring FER System Using Optimal DL-Features

Alhajlah¹

2023

Computers, Materials &Amp; Continua

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Automated Facial Expression Recognition (FER) serves as the backbone of patient monitoring systems, security, and surveillance systems. Real-time FER is a challenging task, due to the uncontrolled nature of the environment and poor quality of input frames. In this paper, a novel FER framework has been proposed for patient monitoring. Preprocessing is performed using contrast-limited adaptive enhancement and the dataset is balanced using augmentation. Two lightweight efficient Convolution Neural Network (CNN) models MobileNetV2 and Neural search Architecture Network Mobile (NasNetMobile) are trained, and feature vectors are extracted. The Whale Optimization Algorithm (WOA) is utilized to remove irrelevant features from these vectors. Finally, the optimized features are serially fused to pass them to the classifier. A comprehensive set of experiments were carried out for the evaluation of real-time image datasets FER-2013, MMA, and CK+ to report performance based on various metrics. Accuracy results show that the proposed model has achieved 82.5% accuracy and performed better in comparison to the state-of-the-art classification techniques in terms of accuracy. We would like to highlight that the proposed technique has achieved better accuracy by using 2.8 times lesser number of features.

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“…In [36], the so called advanced softmax loss was proposed to reduce the negative effects caused by data imbalance, particularly when the classifier is biased toward the majority class. In [37], the authors synthesized facial affects from a neutral image for data augmentation in FER systems.…”

Section: A Fer With Deep Learningmentioning

confidence: 99%

FaceTopoNet: Facial Expression Recognition Using Face Topology Learning

Kolahdouzi

Sepas-Moghaddam

Etemad³

2023

IEEE Trans. Artif. Intell.

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Prior work has shown that the order in which different components of the face are learned using a sequential learner can play an important role in the performance of facial expression recognition systems. We propose FaceTopoNet, an endto-end deep model for facial expression recognition, which is capable of learning an effective tree topology of the face. Our model then traverses the learned tree to generate a sequence, which is then used to form an embedding to feed a sequential learner. The devised model adopts one stream for learning structure and one stream for learning texture. The structure stream focuses on the positions of the facial landmarks, while the main focus of the texture stream is on the patches around the landmarks to learn textural information. We then fuse the outputs of the two streams by utilizing an effective attention-based fusion strategy. We perform extensive experiments on four largescale in-the-wild facial expression datasets -namely AffectNet, FER2013, ExpW, and RAF-DB -and one lab-controlled dataset (CK+) to evaluate our approach. FaceTopoNet achieves state-ofthe-art performance on three of the five datasets and obtains competitive results on the other two datasets. We also perform rigorous ablation and sensitivity experiments to evaluate the impact of different components and parameters in our model. Lastly, we perform robustness experiments and demonstrate that FaceTopoNet is more robust against occlusions in comparison to other leading methods in the area.Impact Statement-Systems that perform facial expression recognition (FER) have a wide variety of applications in realworld problems such as human-machine interaction, robotics, mental health management systems, to name but a few. Our proposed solution achieves better recognition rates in comparison to many other methods in the field of FER, allowing for development of more accurate systems. Moreover, our proposed method demonstrates more robustness against occlusions, which can be beneficial for in-the-wild scenarios. Lastly, our model can learn trees that are optimized, yet also generalize well across different datasets and perform considerably better than humandesigned face trees.

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Accurate and Reliable Facial Expression Recognition Using Advanced Softmax Loss With Fixed Weights

Cited by 21 publications

References 26 publications

Facial Expression Recognition Based on Deep Learning Convolution Neural Network: A Review

Facial Expression Recognition Based on Deep Learning Convolution Neural Network: A Review

A Novel Efficient Patient Monitoring FER System Using Optimal DL-Features

FaceTopoNet: Facial Expression Recognition Using Face Topology Learning

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