Facial Expression Analysis Using PCA

Lekshmi, V. Praseeda; Sasikumar, M.; Vidyadharan, Divya S.; Naveen, S.

doi:10.1007/978-90-481-2311-7_30

Cited by 3 publications

(4 citation statements)

References 5 publications

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“…This analysis permits robust and consistent identification of regions in the image in which eye characteristics can be further extracted (cf. Lata et al 2009, Lee et al 2009, Lekshmi et al 2008, Mena-Chalco et al 2009 for further details on related applications of this method). The application of PCA to facial feature recognition and monitoring is similar to its application in empirical research in operations management studies (cf.…”

Section: Pupillometric Assessmentmentioning

confidence: 99%

Linking Task Conditions to Physiology and Judgment Errors in RM Systems

Bendoly

2011

Production and Operations Management

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In models of optimal decision making, assumptions about managerial behavior are often made with the hope that the prescriptions offered by these models will be effective in practice, even if actual behavior occasionally strays from these assumptions. However, recent revenue management (RM) research has demonstrated what appear to be systematic deviations from normative models of decision making. These deviations can even be observed in relatively simple RM contexts. We suggest that technical errors in capacity allocation decisions are linked to issues such as arousal and stress associated with state conditions of RM tasks. Our study goes beyond existing findings by considering behavioral phenomena in concurrent task settings, where the decision maker is faced with managing decisions for more than one product or service. Physiological measures of eye dilation and blink rate are used as markers of arousal and stress in subjects engaged in RM tasks. Our analysis shows that physiological responses are indeed associated with both the state conditions of RM tasks and the number of capacity blocks managed concurrently by an individual. Deviations from modeled decision making appear to be significantly dependent upon these physiological responses. We conclude with a discussion of implications for further research and practice.

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Section: Pupillometric Assessmentmentioning

confidence: 99%

Linking Task Conditions to Physiology and Judgment Errors in RM Systems

Bendoly

2011

Production and Operations Management

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“…These indicators serve as crucial criteria for evaluation, providing an in-depth understanding of the effectiveness of the strategy. True negative (TN), false positive (FP), false-negative (FN), and true positive (TP) among others, were the four unique parameters which make up the evaluation [23,24].…”

Section: Experimental Results Analysismentioning

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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“…Thus, the proposed WPLBP features can be obtained by using (16) which is logically more realistic. Experimental results show the efficacy of the proposed WPLBP features.…”

Section: Fig 3 Graphical Representation Of the Proposed Ire Models Fo...mentioning

confidence: 99%

“…Gabor wavelet is another popular texture descriptor used by many of the researchers for facial expressions recognition [13][14][15][16]. In these methods, bank of Gabor filters are created by tuning, scaling, and orientation parameters.…”

Section: Introductionmentioning

confidence: 99%

Extraction of informative regions of a face for facial expression recognition

Kumar

Bhuyan

Chakraborty

2016

IET Computer Vision

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The aim of facial expression recognition (FER) algorithms is to extract discriminative features of a face. However, discriminative features for FER can only be obtained from the informative regions of a face. Also, each of the facial subregions have different impacts on different facial expressions. Local binary pattern (LBP) based FER techniques extract texture features from all the regions of a face, and subsequently the features are stacked sequentially. This process generates the correlated features among different expressions, and hence affects the accuracy. This research moves toward addressing these issues. The authors' approach entails extracting discriminative features from the informative regions of a face. In this view, they propose an informative region extraction model, which models the importance of facial regions based on the projection of the expressive face images onto the neural face images. However, in practical scenarios, neutral images may not be available, and therefore the authors propose to estimate a common reference image using Procrustes analysis. Subsequently, weighted‐projection‐based LBP feature is derived from the informative regions of the face and their associated weights. This feature extraction method reduces miss‐classification among different classes of expressions. Experimental results on standard datasets show the efficacy of the proposed method.

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Facial Expression Analysis Using PCA

Cited by 3 publications

References 5 publications

Linking Task Conditions to Physiology and Judgment Errors in RM Systems

Linking Task Conditions to Physiology and Judgment Errors in RM Systems

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

Extraction of informative regions of a face for facial expression recognition

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