Dew Computing-Inspired Mental Health Monitoring System Framework Powered by a Lightweight CNN

Podder, Tanusree; Bhattacharya, Diptendu; Majumdar, Angshuman

doi:10.1007/978-981-19-2177-3_31

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…Podder et al [21] used real-time facial expressions to assist in representing patients' behavioral psychology. Unlike standard lightweight convolutional neural networks (CNNs), the proposed one employs a bypass connection to improve gradient propagation through the network, resulting in higher accuracy.…”

Section: Related Workmentioning

confidence: 99%

Improving Facial Expression Recognition Using HOG with SVM and Modified Datasets Classified by Alexnet

Al-Atroshi,

Ali

2023

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Facial expressions are one of the communication ways between humans and their behavior can be determined through their facial expressions. Recently computer technology has been used to identify the facial expressions of people in order to predict their intentions. It remains a challenge for facial expression recognition to extract discriminative features from training sets with few labels because most deep learning-based algorithms primarily rely on spatial information and large labels. In this paper, we propose a system to classify seven types of facial expressions (Angry, Sadness, Surprise, Happiness, Fear, Neutral, and Disgust) instead of six, as in most previous research. In the proposed system, machine learning algorithms with deep learning are used to increase classification accuracy based on removing some unimportant facial regions. The support vector machine (SVM) algorithm is trained to detect the eyes and mouth regions from the face depending on histogram-oriented gradient (HOG) which is used as a features extractor. Then, merge the eyes and mouth regions for each image to create a new form of an image. After that, five different types of images are generated from the merged image named (RGB, HSV, Gray, Binary, and YCbCr). The images are fed one by one into the convolution neural network (CNN) algorithm. Finally, the voting process is used to select the most predictive class. The proposed system has been tested on three different types of datasets (KDEF, JAFFE, and FER2013) and the prediction accuracy in the system has reached more than 98% in all used datasets. The conclusion is that eliminating unimportant regions impacts the results of the classification accuracy.

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Section: Related Workmentioning

confidence: 99%

Improving Facial Expression Recognition Using HOG with SVM and Modified Datasets Classified by Alexnet

Al-Atroshi,

Ali

2023

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“…A lightweight CNN model for mental health monitoring in a cloud-based environment is proposed in Podder, Bhattacharya & Majumdar (2022a) . However, in Saurav et al (2022) , a dual-integrated lightweight model is deployed for real-time facial expression recognition in wild environments.…”

Section: Literature Surveymentioning

confidence: 99%

A feature boosted deep learning method for automatic facial expression recognition

Podder

Bhattacharya

Majumder

et al. 2023

PeerJ Computer Science

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Automatic facial expression recognition (FER) plays a crucial role in human-computer based applications such as psychiatric treatment, classroom assessment, surveillance systems, and many others. However, automatic FER is challenging in real-time environment. The traditional methods used handcrafted methods for FER but mostly failed to produce superior results in the wild environment. In this regard, a deep learning-based FER approach with minimal parameters is proposed, which gives better results for lab-controlled and wild datasets. The method uses features boosting module with skip connections which help to focus on expression-specific features. The proposed approach is applied to FER-2013 (wild dataset), JAFFE (lab-controlled), and CK+ (lab-controlled) datasets which achieve accuracy of 70.21%, 96.16%, and 96.52%. The observed experimental results demonstrate that the proposed method outperforms the other related research concerning accuracy and time.

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DewMetrics: Demystification of the Dew Computing in Sustainable Internet of Things

Roy,

Panda,

Kim

et al. 2023

Internet of Things

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Dew Computing-Inspired Mental Health Monitoring System Framework Powered by a Lightweight CNN

Cited by 5 publications

References 24 publications

Improving Facial Expression Recognition Using HOG with SVM and Modified Datasets Classified by Alexnet

Improving Facial Expression Recognition Using HOG with SVM and Modified Datasets Classified by Alexnet

A feature boosted deep learning method for automatic facial expression recognition

DewMetrics: Demystification of the Dew Computing in Sustainable Internet of Things

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