Facial Expression Recognition Using DCNN and Development of an iOS App for Children with ASD to Enhance Communication Abilities

Haque, Inzamam Ul; Valles, Damian

doi:10.1109/uemcon47517.2019.8993051

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…Ul Haque e Valles [32] construíram um modelo Deep Convolutional Neural Network (DCNN) baseado na arquitetura VGG-16. Em relac ¸ão a trabalhos anteriores, a fim de melhorar a precisão e o desempenho, foram modificados parâmetros do modelo, como a taxa de aprendizagem, o tamanho de lote, o número de épocas e a taxa de dropout.…”

Section: Revis ãO Da Literaturaunclassified

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Facial Expression Recognition to Aid Visually Impaired People

Silva¹,

Silva²,

Veras³

et al. 2021

Anais Do XVII Workshop De Visão Computacional (WVC 2021)

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Facial expression recognition systems can help a visually impaired person to identify the emotions of the person with whom she interacts, assisting in her non-verbal communication. Among the various researches carried out in recent years on recognition of facial expressions, the best results obtained come from methods that use deep learning, mainly with the use of convolutional neural networks. This work presents a literature review on the problem of recognition of facial expressions, through the use of convolutional neural networks and proposes two approaches in which the first one uses pre-trained CNN models together with the Linear SVM classifier that, applied to the bases CK+ and JAFFE data, obtained maximum accuracy of 89.6% and 95.7%, respectively. And in the second approach, a CNN model built from scratch is used with the CK+ and FER2013 databases, which obtained accuracy rates of 85% and 65.8%, respectively.

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Section: Revis ãO Da Literaturaunclassified

“…Abordagem de Ul Haque e Valles [32] foca no problema de variac ¸ão de iluminac ¸ão realizando testes com imagens em diferentes condic ¸ões de iluminac ¸ão. Wu et al [16] visam obter bons resultados, concentrando-se no problema de diferentes poses de cabec ¸a e identificando pontos de referências espaciais.…”

Section: A Discussão Sobre a Revisão De Literaturaunclassified

Facial Expression Recognition to Aid Visually Impaired People

Silva¹,

Silva²,

Veras³

et al. 2021

Anais Do XVII Workshop De Visão Computacional (WVC 2021)

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“…Use of BCI systems provides insight into the user's inner-emotional state. Valles et al [72] conducted research focused on mobile software design to provide assistance to children with ASD. They aimed to design a smart iOS app based on facial images according to Figure 11.…”

Section: A Mobile and Software Applicationsmentioning

confidence: 99%

“…In this way, people's faces at different angles and brightness are first photographed, and are turned into various emoji so that the autistic child can express his/her feelings and emotions. In this group's investigation [72], Kaggle's (The Facial Expression Recognition 2013) and KDEF (Kaggle's FER2013 and Karolinska Directed Emotional Faces) databases were used to train the VGG-16. In addition, the LEAP system was adapted to train the model at the University of Texas.…”

Section: A Mobile and Software Applicationsmentioning

confidence: 99%

Deep Learning for Neuroimaging-based Diagnosis and Rehabilitation of Autism Spectrum Disorder: A Review

Khodatars,

Shoeibi,

Sadeghi

et al. 2020

Preprint

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Accurate diagnosis of Autism Spectrum Disorder (ASD) is essential for its management and rehabilitation. Neuroimaging techniques that are non-invasive are disease markers and may be leveraged to aid ASD diagnosis. Structural and functional neuroimaging techniques provide physicians substantial information about the structure (anatomy and structural connectivity) and function (activity and functional connectivity) of the brain. Due to the intricate structure and function of the brain, diagnosing ASD with neuroimaging data without exploiting artificial intelligence (AI) techniques is extremely challenging. AI techniques comprise traditional machine learning (ML) approaches and deep learning (DL) techniques. Conventional ML methods employ various feature extraction and classification techniques, but in DL, the process of feature extraction and classification is accomplished intelligently and integrally. In this paper, studies conducted with the aid of DL networks to distinguish ASD were investigated. Rehabilitation tools provided by supporting ASD patients utilizing DL networks were also assessed. Finally, we presented important challenges in the automated detection and rehabilitation of ASD.

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An Audio Processing Approach using Ensemble Learning for Speech-Emotion Recognition for Children with ASD

Valles

Matin

2021

2021 IEEE World AI IoT Congress (AIIoT)

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Facial Expression Recognition Using DCNN and Development of an iOS App for Children with ASD to Enhance Communication Abilities

Cited by 6 publications

References 13 publications

Facial Expression Recognition to Aid Visually Impaired People

Facial Expression Recognition to Aid Visually Impaired People

Deep Learning for Neuroimaging-based Diagnosis and Rehabilitation of Autism Spectrum Disorder: A Review

An Audio Processing Approach using Ensemble Learning for Speech-Emotion Recognition for Children with ASD

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