Research on face detection method based on improved MTCNN network

Wang, Yang; Yuan, Guowu; Zheng, Dong; Wu, Hao; Pu, Yuanyuan; Xu, Dan

doi:10.1117/12.2539617

Cited by 3 publications

(5 citation statements)

References 4 publications

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“…A convolutional neural network (CNN) is a multi-layered neural network that draws biological inspiration from the visual cortex in animals [19]. CNN architecture is especially valuable in image processing applications, providing good results across many studies [20], and this is the architecture deployed in this study.…”

Section: Neural Network-based Deep Learningmentioning

confidence: 99%

“…In facial recognition work, facial landmarks, defined as the detection and localization of certain characteristic points on the face, are considered very important features and are widely used in classification [20].…”

Section: Features Extractionmentioning

confidence: 99%

“…Open-access software packages that automatically and efficiently detect facial landmarks include OpenCV, Imotion, MTCNN, and Open Face [20]. One of the main variants between them is the number of landmarks they detect; while Imotion detects 34, the MTCNN algorithm detects just five.…”

Section: Features Extractionmentioning

confidence: 99%

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Facial Emotion Recognition-based Engagement De-tection in Autism Spectrum Disorder

Alhakbani

2024

IJACSA

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Engagement is the state of alertness that a person experiences and the deliberate focus of their attention on a task-relevant stimulus. It positively correlates with many aspects such as learning, social support, and acceptance. Facial emotion recognition using artificial intelligence can be beneficial to automatically measure individual engagement especially when using automated learning and playing modalities such as using Robots. In this study, we proposed an automatic engagement detection model through facial emotional recognition, particularly in determining autistic children's engagement. The methodology employed a transfer learning approach at the dataset level, utilizing facial image datasets from typically developing (TD) children and children with ASD. The classification task was performed using convolutional neural network (CNN) methods. Comparative analysis revealed that the CNN method demonstrated superior accuracy compared to random forest (RF), support vector machine (SVM), and decision tree algorithms in both the TD and ASD datasets. The findings highlight the potential of CNN-based facial emotion recognition for accurately assessing engagement in children with ASD, with implications for enhancing learning, social support, and acceptance in this population. This research contributes to the field of engagement measurement in autism and underscores the importance of leveraging AI techniques for improving understanding and support for children with ASD.

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Section: Neural Network-based Deep Learningmentioning

confidence: 99%

Section: Features Extractionmentioning

confidence: 99%

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Facial Emotion Recognition-based Engagement De-tection in Autism Spectrum Disorder

Alhakbani

2024

IJACSA

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“…Sci. 2023, 13, x FOR PEER REVIEW 7 of 17 specific task, such as convolution or pooling [30,31]. The advantage of using a CNN is that it can achieve state-of-the-art performance on a wide range of image recognition tasks, such as object detection, face recognition, and medical image analysis, with relatively little manual intervention.…”

Section: Emotion Modelmentioning

confidence: 99%

“…The main idea behind a CNN is that it can automatically learn to detect useful features in images, such as edges, corners, and textures, without the need for manual feature engineering. This is accomplished through the use of a specialized architecture that consists of multiple layers of interconnected neurons, each of which performs a specific task, such as convolution or pooling [ 30 , 31 ]. The advantage of using a CNN is that it can achieve state-of-the-art performance on a wide range of image recognition tasks, such as object detection, face recognition, and medical image analysis, with relatively little manual intervention.…”

Section: System Analysis and Designmentioning

confidence: 99%

Measuring Engagement in Robot-Assisted Therapy for Autistic Children

Al-Nafjan

Alhakbani

Alabdulkareem

2023

Behavioral Sciences

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Children with autism face a range of challenges when it comes to verbal and nonverbal communication. It is essential that children participate in a variety of social, educational, and therapeutic activities to acquire knowledge that is essential for cognitive and social development. Recent studies have shown that children with autism may be interested in playing with an interactive robot. The robot can engage these children in ways that demonstrate and train essential aspects of human interaction, guiding them in therapeutic sessions to practice more complex forms of interaction found in social human-to-human interactions. This study sets out to investigate Robot-Assisted Autism Therapy (RAAT) and the use of artificial intelligence (AI) approaches for measuring the engagement of children during therapy sessions. The study population consisted of five native Arabic-speaking autistic children aged between 4 and 11 years old. The child–robot interaction was recorded by the robot camera and later used for analysis to detect engagement. The results show that the proposed system offers some accuracy in measuring the engagement of children with ASD. Our findings revealed that robot-assisted therapy is a promising field of application for intelligent social robots, especially to support autistic children in achieving their therapeutic and educational objectives.

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Design Integrated Staff Welcoming and Administration System Based on Facial Recognition for Smart University

Tran

et al. 2020

2020 5th International Conference on Green Technology and Sustainable Development (GTSD)

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Research on face detection method based on improved MTCNN network

Cited by 3 publications

References 4 publications

Facial Emotion Recognition-based Engagement De-tection in Autism Spectrum Disorder

Facial Emotion Recognition-based Engagement De-tection in Autism Spectrum Disorder

Measuring Engagement in Robot-Assisted Therapy for Autistic Children

Design Integrated Staff Welcoming and Administration System Based on Facial Recognition for Smart University

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