Facial Affect Recognition Using Regularized Discriminant Analysis-Based Algorithms

Lee, Chien-Cheng; Huang, Shin-Sheng; Shih, Cheng-Yuan

doi:10.1155/2010/596842

Cited by 11 publications

(5 citation statements)

References 35 publications

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“…One disturbing problem that researchers of facial expression recognitions face is an attempt to make realistic comparisons of performances of proposed methods with the existing ones. There is lack of common benchmark to compare results hence various research groups use their own methods, but in doing so, cannot make direct comparisons since the choice of the methods and databases differs from one research group to the other (Shan et al, 2009;Lee, Huang & Shih, 2010). After all, upon what bases can one compare results if there are dissimilarities of testing?…”

Section: Comparing Results Of Expression Recognition Systemsmentioning

confidence: 99%

Facial Expression Recognition – A Comprehensive Review

Owusu

Gavua

Zhan

2020

IJTMR

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In this paper, we have provided a comprehensive review of modern facial expression recognition system. The history of the technology as well as the current status in terms of accomplishments and challenges has been emphasized. First, we highlighted some modern applications of the technology. The best methods of face detection, an essential component of automatic facial expression system, are also discussed. Facial Action Coding Systems- the cumulative database of research and development of micro expressions within the behavioral science are also enlightened. Then various facial expression databases and the types of recognitions are explained in detail. Finally, we provided the procedures of facial expression recognition from feature extraction to classifications, emphasizing on modern and best approaches. Then the challenges encountered when comparing results with others are highlighted and suggestions to alleviate the problems, provided. Keywords: FACS; Expression recognition; spatial; spatio-temporal; expression classification

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Section: Comparing Results Of Expression Recognition Systemsmentioning

confidence: 99%

Facial Expression Recognition – A Comprehensive Review

Owusu

Gavua

Zhan

2020

IJTMR

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“…Using a deep learning algorithm together with multi-photon imaging demonstrated potential in differentiating between various forms of hepatocellular carcinoma (HCC), opening up fresh possibilities for automated testing [22]. There is plenty of potential for enhancement of accuracy, given that the study is still in its infancy.…”

Section: Deep Learning In Liver Cancer Diagnosismentioning

confidence: 99%

Automated Classification of Liver Cancer Stages Using Deep Learning on Histopathological Images

Kavitha,

Hussain,

Chillakuru

et al. 2024

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Plagued by a high incidence rate worldwide, liver cancer comes in sixth place among all cancers. The degree of differentiation, which may be roughly divided into three types: weakly moderately differentiated, highly differentiated, and differentiated, has a substantial impact on the malignancy level of this terrible illness. In direction to improve the existence of affected role and life expectancy, therapeutic techniques that are customised for these varied levels of diversity are essential. The gold standard for identifying the main liver cancer, hepatocellular carcinoma (HCC), is a histopathological picture that allows for exact distinction of liver tumours at different stages of development. This study explores the creative use of the R-CNN algorithm for deep learning for the astute categorization of histological pictures associated with liver cancer that undergoes differentiation. So this study compared how well R-CNN did compared to five other popular deep learning models -SKNet, ResNet CBAM, ResNet50, VGG16, and SENet. It was really important to set up a good system to collect a lot of different data for this project. This would make sure they had enough info to properly test how well the different math models worked. They also created a thorough and precise method using things like recall, confusion matrices, F1-scores, and accuracy to analyze how the models performed. The results showed that R-CNN did amazingly well, with an accuracy of 96.7%! That means it was able to classify things correctly almost all the time. This demonstrates it had the most accurate predictions out of everything they looked at. Additionally, the R-CNN model proved to be very reliable and able to generalize well. In other words, it should work just as good on new, unseen data as it did on the information it was originally skilled on. This study compares the routine of R-CNN to five other well-established deep learning models: SKNet, ResNet CBAM, ResNet50, VGG16, and SENet. Developing a robust data collection infrastructure is critical to enable the project to ensure a large and varied dataset for thorough evaluation of the mathematical models under consideration. A comprehensive and precise evaluation approach was provided through judicious usage of metrics like F1-Score, recall, confusion matrix, and accuracy to analyze the model performance. Testing results demonstrate R-CNN's capabilities, evidenced by its notable 96. 7% accuracy indicating highly precise classification outcomes. Furthermore, the model exhibits strong reliability and generalizability.

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“…Bai et al [4] Gabor+LBP+LDA 92% to 97% Zhi and Ruan [30] 2D discriminant LPP 95.91% Zhang et.al. [38] Multilayer Perceptron 90.34% Liejun et al [32] SVM based 95.7% Zhao et al [34] PCA and NMF 93.72% Lee [33] RDAB 96.67% In this work SVM classifier has been implemented to classify the expressions. To create input dataset, all 210 images of JAFFE database and 90 images of YALE database were considered.…”

Section: A Subspace Formationmentioning

confidence: 99%

Facial Expression Recognition by Holistic and Geometrically IntegratedSubspace

Hegde¹,

Seetha²,

Hegde³

2018

IJIGSP

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This paper demonstrates mainly on feature extraction by analytic and holistic methods and proposes a novel approach for feature level fusion for efficient expression recognition. Gabor filter magnitude feature vector is fused with upper part geometrical features and phase feature vector is fused with lower part geometrical features respectively. Both these high dimensional feature dataset has been projected into low dimensional subspace for de-correlating the feature data redundancy by preserving local and global discriminative features of various expression classes of JAFFE, YALE and FD databases. The effectiveness of subspace of fused dataset has been measured with different dimensional parameters of Gabor filter. The experimental results reveal that performance of the subspace approaches for high dimensional proposed feature level fused dataset compared with state of art approaches.

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Facial Affect Recognition Using Regularized Discriminant Analysis-Based Algorithms

Cited by 11 publications

References 35 publications

Facial Expression Recognition – A Comprehensive Review

Facial Expression Recognition – A Comprehensive Review

Automated Classification of Liver Cancer Stages Using Deep Learning on Histopathological Images

Facial Expression Recognition by Holistic and Geometrically IntegratedSubspace

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