Video analytics-based facial emotion recognition system for smart buildings

Gautam, K. S.; Thangavel, Senthil Kumar

doi:10.1080/1206212x.2019.1642438

Cited by 20 publications

(4 citation statements)

References 14 publications

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“…The dataset applied in this study is the Kaggle facial emotion recognition dataset (Fer-2013), which was released by Pieere and Aaron at the ICML2013 seminar and consisted of 35,887 facial emotion pictures. There are 4,953, 547, 5,121, 8,989, 6,077, 4,002, and 6,198 pictures on angry, disgust, fear, happiness, sadness, surprise, and neutral emotions [ 33 ]. The dataset consists of three parts: the training set contains 28,709 pictures, the validating set contains 3,589 pictures, and the testing set contains 3,589 pictures.…”

Section: Methods and Designmentioning

confidence: 99%

“…e dataset applied in this study is the Kaggle facial emotion recognition dataset (Fer-2013), which was released by Pieere and Aaron at the ICML2013 seminar and consisted of 35,887 facial emotion pictures. ere are 4,953, 547, 5,121, 8,989, 6,077, 4,002, and 6,198 pictures on angry, disgust, fear, happiness, sadness, surprise, and neutral emotions [33].…”

Section: Data Training and Parameter Optimizationmentioning

confidence: 99%

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Construction and Analysis of Emotion Recognition and Psychotherapy System of College Students under Convolutional Neural Network and Interactive Technology

Chen

Liang

Xu³

2022

Computational Intelligence and Neuroscience

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This study's aim is to effectively establish a psychological intervention and treatment system for college students and discover and correct their psychological problems encountered in a timely manner. From the perspectives of pedagogy and psychology, the college students majoring in physical education are selected as the research objects, and an interactive college student emotion recognition and psychological intervention system is established based on convolutional neural network (CNN). The system takes face recognition as the data source, adopts feature recognition algorithms to effectively classify the different students, and designs a psychological intervention platform based on interactive technology, and it is compared with existing systems and models to further verify its effectiveness. The results show that the deep learning CNN has better ability to recognize student emotions than backpropagation neural network (BPNN) and decision tree (DT) algorithm. The recognition accuracy (ACC) can be as high as 89.32%. Support vector machine (SVM) algorithm is adopted to classify the emotions, and the recognition ACC is increased by 20%. When the system's K value is 5 and d value is 8, the ACC of the model can reach 92.35%. The use of this system for psychotherapy has a significant effect, and 45% of the students are very satisfied with the human-computer interaction of the system. This study aims to guess the psychology of students through emotion recognition and reduce human participation based on the human-computer interaction, which can provide a new research idea for college psychotherapy. At present, the mental health problems of college students cannot be ignored; especially every year, there will be news reports of college students' extreme behaviors due to depression and other psychological problems. An interactive college student emotion recognition and psychological intervention system based on convolutional neural network (CNN) is established. This system uses face recognition as the basic support technology and uses feature recognition algorithms to effectively classify different students. An interaction technology-based psychological intervention platform is designed and compared with existing systems and models to further verify the effectiveness of the proposed system. The results show that deep learning has better student emotion recognition ability than backpropagation neural network (BPNN) and decision tree algorithm. The recognition accuracy is up to 89.32%. Support vector machine algorithm is employed to classify emotions, and the recognition acceptability rate increases by 20%. When K is 5 and d is 8, the acceptability rate of the model can reach 92.35%. The effect of this system in psychotherapy is remarkable, and 45% of students are very satisfied with the human-computer interaction of this system. This work aims to speculate students' psychology through emotion recognition, reduce people's par...

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Section: Methods and Designmentioning

confidence: 99%

Section: Data Training and Parameter Optimizationmentioning

confidence: 99%

Construction and Analysis of Emotion Recognition and Psychotherapy System of College Students under Convolutional Neural Network and Interactive Technology

Chen

Liang

Xu³

2022

Computational Intelligence and Neuroscience

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“…This paper [28] proposed a video analytics-based facial emotion recognition system for smart buildings. The authors used a dataset of facial expressions to train their machine learning model, which used the Haar Cascade Classifier to detect faces and the Local Binary Patterns Histograms (LBPH) algorithm to recognize facial expressions.…”

Section: Related Workmentioning

confidence: 99%

Improving of Smart Health Houses: Identifying Emotion Recognition using Facial Expression Analysis

SHI,

2024

IJACSA

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Smart health houses have shown great potential for providing advanced healthcare services and support to individuals. Although various computer vision based approaches have been developed, current facial expression analysis methods still have limitations that need to be addressed. This research paper introduces a facial expression analysis technique for emission recognition based on YOLOv4-based algorithm. The proposed method involves the use of a custom dataset for training, validation, and testing of the model. By overcoming the limitations of existing methods, the proposed technique delivers precise and accurate results in detecting subtle changes in facial expressions. Through several experimental and performance evaluation tasks, we have assessed the efficacy of our proposed method and demonstrated its potential to enhance the accuracy of Smart Health Houses. This study emphasizes the importance of addressing emotional well-being in healthcare. As experimental results shown, the prosed method achieved satisfy accuracy rate and the effectiveness of the YOLOv4 model for emotion detection suggests that emotional intelligence training can be a valuable tool in achieving this goal.

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“…Recently, several flame pixel detection methods have been proposed by researchers [17]- [19]. We now present different literature works along with their strengths and limitations.…”

Section: Related Workmentioning

confidence: 99%

Real-time Building Fire Detection and Segmentation in Video using Convolutional Neural Networks with Gaussian threshold approach

Sridhar

Thangavel

Parameswaran

et al. 2022

Preprint

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Computer vision-based fire flame detection system has recently begun to replace traditional fire detection systems. The conventional imaging-based system uses color models and temporal analysis algorithms to detect and segment fire in an image or a frame. Color model algorithms could not effectively distinguish between fire and fire-like objects in real-time videos. Besides, temporal analysis between structures has not yielded higher detection accuracies. This work proposes a lightweight convolutional neural network (CNN) combined with Gaussian distribution threshold probability to detect and segment the fire in a frame sequence. The proposed hybrid work extracted the features and automatically classified the frames using simple network architecture, and the Gaussian probability threshold method improves the detection of fire region localization. Experimental performance of the proposed work increases the detection accuracy and better false alarm performance. This paper can predict the severity of the fire, area, and centroid, fire probability. The proposed work using low-resolution frames will surely help the real-time implementation system.

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Video analytics-based facial emotion recognition system for smart buildings

Cited by 20 publications

References 14 publications

Construction and Analysis of Emotion Recognition and Psychotherapy System of College Students under Convolutional Neural Network and Interactive Technology

Construction and Analysis of Emotion Recognition and Psychotherapy System of College Students under Convolutional Neural Network and Interactive Technology

Improving of Smart Health Houses: Identifying Emotion Recognition using Facial Expression Analysis

Real-time Building Fire Detection and Segmentation in Video using Convolutional Neural Networks with Gaussian threshold approach

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