Design and Evaluation of a Deep Learning Algorithm for Emotion Recognition

Subramanian, R. Raja; Niharika, Chunduri Sandya; Rani, Dondapati Usha; Pavani, P. Geetha; Syamala, K.

doi:10.1109/iciccs51141.2021.9432336

Cited by 14 publications

(5 citation statements)

References 4 publications

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“…Also, the model is unable to distinguish between voluntary (deceptive) and involuntary (natural) facial expressions. CNN 44 model built with the help of Keras, TensorFlow, and OpenCV detects emotions in the face with high accuracy but takes much time to train the data in comparison to our method and predicts inaccurate results for the normal face (surprised) and when having a face with sweat and oils as shown in Fig. 6.…”

Section: Qualitative Resultsmentioning

confidence: 97%

“…The proposed model is compared with seven state-of-the-art models. CNN, 41 fisherface + HOG (one-versus-all approach), 42 CNNs and BOVW + local SVM, 43 CNN (three layers), 44 DCNN, 38 fusion feature (CNN + LBP + ORB) + ConvNet, 39 and mini-Xception 45 . The results are obtained by compiling the authors’ source code and with reference from their Google Drive.…”

Section: Experimentation and Evaluationmentioning

confidence: 99%

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Real-time emotion recognition using end-to-end attention-based fusion network

et al. 2023

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.Real-time emotion detection based on facial expression is an innovative research field that has been applied in several areas, such as health, human–machine vision, and autonomous safety. Researchers in object detection are involved in developing methods to interpret, code facial expressions, and extract these features to be better predicted by machines. Furthermore, the success of deep learning with different architectures is exploited to achieve better performance. But these methods drastically fail in excessive sweating in different health conditions. We aim to create a dataset in different health conditions and detect facial emotion using the encoder and decoder-based deep learning methodology. The proposed architecture and the dataset present the progress made by comparing the other proposed methods and the quantitative and qualitative results obtained. The major benefit of our study is to enhance the emotion detection efficiency with other proposed methods and real-time applications for different health conditions. We propose the application of feature extraction of facial expressions with an end-to-end attention module-based fusion network for detecting different facial emotions (happy, angry, neutral, surprised, etc.) with an accuracy of 99.68%. The proposed system depends upon the human face; as we know, the face reflects human brain activities or emotions.

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Section: Qualitative Resultsmentioning

confidence: 97%

Section: Experimentation and Evaluationmentioning

confidence: 99%

Real-time emotion recognition using end-to-end attention-based fusion network

et al. 2023

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“…Initially, we evaluated the model's performance against FER-2013 to measure its resilience. Our proposed model demonstrated superior performance compared to models labeled as [34,[58][59][60][61][62][63][64], and [3], achieving accuracy improvements of 25 73.00% [3] 73.40% [56] 70.00% [57] 64.70% Model in this paper 91.71%…”

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

confidence: 83%

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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“…We conducted several experiments to evaluate the performance of the proposed FER model with other state-of-the-art methods, as shown in Table 9. To evaluate the model's robustness, we first compared the performance of our model with FER-2013; the proposed model surpassed an accuracy of 23.2%, 17.2%, 0.6%, and 2.42%, respectively, compared to the models of Arriaga et al [51], J. Li et al [52], Subramanian et al [53], and Borgalli et al [46]. 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.…”

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

confidence: 99%

“…Arriaga et al [51] Mini-Xception 66.0 J. Li et al [52] CNN with Transfer Learning 72.0 Subramanian et al [53] Three Layer CNN architecture 88.6 Borgalli et al [46] Six We evaluated the performance of a proposed model in real time to compute the processing time of the proposed model over GPU, CPU and resource-constrained device (Jetson Nano). Jetson Nano is a small and powerful computer that runs multiple CNNs in parallel for different applications, such as recognition, segmentation, object detection, and speech processing.…”

Section: Tablementioning

confidence: 99%

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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Design and Evaluation of a Deep Learning Algorithm for Emotion Recognition

Cited by 14 publications

References 4 publications

Real-time emotion recognition using end-to-end attention-based fusion network

Real-time emotion recognition using end-to-end attention-based fusion network

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

Facial Emotion Recognition in Verbal Communication Based on Deep Learning

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