A Deep Learning Framework with Cross Pooled Soft Attention for Facial Expression Recognition

Bodapati, Jyostna Devi; Naik, Dinesh; Suvarna, B.; Naralasetti, Veeranjaneyulu

doi:10.1007/s40031-022-00746-2

Cited by 12 publications

(8 citation statements)

References 39 publications

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“…We also assessed the robustness of our model using the CK+ dataset, where our model achieved a promising result compared to state-of-the-art methods. The proposed model achieved 1.48%, 9.98%, and 3.29% higher accuracy compared with those of Hasani et al [54], Borgalli et al [46], and Bodapati et al [6], respectively. We then assessed the proposed model using the KDEF dataset.…”

Section: Comparative Analysis Of the Proposed Model With State-of-the...mentioning

confidence: 56%

“…Vision sensorbased FER has attracted attention in current research and has great potential in real-time FER recognition. In vision-based FER, the researchers focused on seven basic expressions, i.e., anger, disgust, fear, happy, neutral, sad, and surprise [5,6], and they categorised the FER into two sub-categories as conventional and deep learning (DL)-based methods.…”

Section: Introductionmentioning

confidence: 99%

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Facial Emotion Recognition in Verbal Communication Based on Deep Learning

Alsharekh

2022

Sensors

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Facial emotion recognition from facial images is considered a challenging task due to the unpredictable nature of human facial expressions. The current literature on emotion classification has achieved high performance over deep learning (DL)-based models. However, the issue of performance degradation occurs in these models due to the poor selection of layers in the convolutional neural network (CNN) model. To address this issue, we propose an efficient DL technique using a CNN model to classify emotions from facial images. The proposed algorithm is an improved network architecture of its kind developed to process aggregated expressions produced by the Viola–Jones (VJ) face detector. The internal architecture of the proposed model was finalised after performing a set of experiments to determine the optimal model. The results of this work were generated through subjective and objective performance. An analysis of the results presented herein establishes the reliability of each type of emotion, along with its intensity and classification. The proposed model is benchmarked against state-of-the-art techniques and evaluated on the FER-2013, CK+, and KDEF datasets. The utility of these findings lies in their application by law-enforcing bodies in smart cities.

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Section: Comparative Analysis Of the Proposed Model With State-of-the...mentioning

confidence: 56%

Section: Introductionmentioning

confidence: 99%

Facial Emotion Recognition in Verbal Communication Based on Deep Learning

Alsharekh

2022

Sensors

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“…6.29%, and 8.6% when compared to models referenced as [3,34,61,63,65], and [27] respectively. We evaluated the model's robustness by examining its performance with the KDEF dataset.…”

Section: Performing a Comparative Analysis Of The Proposed Model Agai...mentioning

confidence: 93%

Revolutionizing online education: Advanced facial expression recognition for real-time student progress tracking via deep learning model

Aly

2024

Multimed Tools Appl

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This paper presents a groundbreaking online educational platform that utilizes facial expression recognition technology to track the progress of students within the classroom environment. Through periodic image capture and facial data extraction, the platform employs ResNet50, CBAM, and TCNs for enhanced facial expression recognition. Achieving accuracies of 91.86%, 91.71%, 95.85%, and 97.08% on the RAF-DB, FER2013, CK + , and KDEF expression datasets, respectively, the proposed model surpasses the initial ResNet50 model in accuracy and detection of students' learning states. Comparative evaluations against state-of-the-art models using the RAF-DB, FER2013, CK + , and KDEF datasets underscore the significance of the results for educational institutions. By enhancing emotion recognition accuracy, improving feature relevance, capturing temporal dynamics, enabling real-time monitoring, and ensuring robustness and adaptability in online classroom environments, this approach offers valuable insights for educators to enhance teaching strategies and student outcomes. The combined capabilities of ResNet50, CBAM, and TCNs contribute uniquely to capturing dynamic changes in facial expressions over time, thereby facilitating accurate interpretation of students' emotions and engagement levels for more effective monitoring of learning behaviors in real-time.

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“…Several challenges arise due to the irregular geometry of salt structures, complex overburden and under burden formations, and the presence of seismic noise. Seismic images are visual representations of subsurface geological structures and formations obtained through the use of seismic exploration techniques [2]. These images are crucial tools for understanding the Earth's subsurface, particularly in locating and assessing potential hydrocarbon reservoirs [3].…”

Section: Section 1: Introductionmentioning

confidence: 99%

Automatic Salt Segmentation Using Deep Learning Techniques

Jadhav,

Shah,

Vaghani

et al. 2024

Preprint

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Detecting salt boundaries in seismic images is critical for subsurface reservoir characterization and oil and gas exploration. The presence of salt in seismic data often indicates the presence of valuable hydrocarbon resources, which could lead to significant oil discoveries. Traditional manual interpretation methods have limitations, prompting the industry to embrace deep learning techniques. Our proposed system extensively evaluates the effectiveness of deep learning models in salt boundary detection. Our approach involves developing a custom residual encoder-decoder model and comparing it against two existing models: Res-UNet and UNet. The advantage of our custom-built residual encoder-decoder model lies in its utilization of transposed convolutions for image segmentation. Unlike regular convolutions that extract features and reduce image size, transposed convolutions expand the image, potentially introducing new information from seismic data. The custom model emerges superior to UNet and Res-UNet models, exhibiting an accuracy of 94.28% and a precision score of 0.86. A series of comparative analysis is drawn with a main focus on transforming the automated salt segmentation process.

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A Deep Learning Framework with Cross Pooled Soft Attention for Facial Expression Recognition

Cited by 12 publications

References 39 publications

Facial Emotion Recognition in Verbal Communication Based on Deep Learning

Facial Emotion Recognition in Verbal Communication Based on Deep Learning

Revolutionizing online education: Advanced facial expression recognition for real-time student progress tracking via deep learning model

Automatic Salt Segmentation Using Deep Learning Techniques

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