Emotion Detection of Autistic Children Using Image Processing

Rani, P. Esther

doi:10.1109/iciip47207.2019.8985706

Cited by 22 publications

(9 citation statements)

References 5 publications

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“…Največ se uporablja v medicini za sintezo slik in posledično za pridobivanje večje količine podatkov (Yi in sod., 2019). Članek (Rani, 2014) govori o generiranju slik otrok. Bolj specifično tistih, ki imajo avtizem.…”

Section: Sorodna Delaunclassified

ROSUS 2023 - Računalniška obdelava slik in njena uporaba v Sloveniji 2023: Zbornik 17. strokovne konference

2023

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the development group, Vision Lab Slovenia, is part of a global development team working in the field of autonomous perception. Industrial automation is changing at an extremely fast pace. As an innovation leader at the forefront of the development of state-of-the-art sensor technology, SICK today offers solutions that are ready for the challenges of the futurewith intelligent sensors that collect data, evaluate it in real time, adapt to their environment and communicate in the network. In this paper, solutions based on image processing technologies are presented in more detail. Approaches are highlighted that support modularity and flexibility concepts through software, giving users a high degree of flexibility in solutions for specific requirements.

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Section: Sorodna Delaunclassified

ROSUS 2023 - Računalniška obdelava slik in njena uporaba v Sloveniji 2023: Zbornik 17. strokovne konference

2023

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“…The emotions were grouped regarding their similarity. [21,24] attention (1): [25] bored (1): [25] fear-related 16 fear ( 12): [17,[26][27][28][29][30][31][32][33][34][35][36] anxiety (3): [20,22,25] trepidation (1): [37] joy-related 21 joy ( 5): [21,[26][27][28][29] liking (3): [19,20,22] happiness (12): [17,24,25,[30][31][32][33][34][35][37][38][39] smile (1): [40] relaxation (1): [37] There are studies that treat attention, engagement, involvement, and boredom as emotional states. They are more like mental states; however, they are emotion-related and were used in the context of emotion recognition in the analyzed papers.…”

Section: Emotions Recognizedmentioning

confidence: 99%

“…joy 21 [17,[19][20][21][22][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39] anger 12 [17,[27][28][29][30][31][32][33]35,36,38,39] fear 12 [17,[26][27][28][29][30][31][32][33][34][35][36] disgust 7 [17,25,27,29,32,35,36] sadness 16 [17,…”

Section: Number Of Papers Papersmentioning

confidence: 99%

“…Number of Papers Papers joy 3 [19,20,22] joy, neutral 2 [21,24] joy, sadness 2 [21,37] joy, fear, sadness 2 [26,34] joy, disgust, sadness, neutral 1 [25] joy, anger, sadness, neutral 2 [38,39] joy, anger, fear, sadness, neutral 2 [28,30] joy, anger, fear, disgust, sadness, surprise 1 [17] joy, anger, fear, sadness, surprise, neutral 2 [31,33] joy, anger, fear, disgust, sadness, neutral 1 [36] joy, anger, fear, disgust, sadness, surprise, netural 4 [27,29,32,35] In some papers, only two-class recognition was performed. In those papers, joy is recognized as a class combined with no-joy in three papers, with a neutral state in two papers, and with sadness in two papers.…”

Section: Emotions Recognized Togethermentioning

confidence: 99%

“…Therefore, other vocalisations and physiological responses of the nervous system are more frequently used. depth video [29,31] images [39] EMG [40] body posture RGB video [23,42] depth video [42] eye gaze RGB video [17] head movement RGB video [23,28] gestures RGB video [23,28,42] depth video [42] motion RGB video [17,23,42] depth video [17,42] sound prosody of speech audio [17,23,25,30,36,41] vocalization audio [17,23,25,36,41] heart activity heart rate ECG [19][20][21][22]43] BVP [21,23,24] variability HRV ECG [19,20,22,43] BVP [21,24] muscle activities muscle tension EMG [19][20][21]…”

Section: Channels Used For Emotion Recognitionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic Emotion Recognition in Children with Autism: A Systematic Literature Review

Landowska

Karpus

Zawadzka

et al. 2022

Sensors

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The automatic emotion recognition domain brings new methods and technologies that might be used to enhance therapy of children with autism. The paper aims at the exploration of methods and tools used to recognize emotions in children. It presents a literature review study that was performed using a systematic approach and PRISMA methodology for reporting quantitative and qualitative results. Diverse observation channels and modalities are used in the analyzed studies, including facial expressions, prosody of speech, and physiological signals. Regarding representation models, the basic emotions are the most frequently recognized, especially happiness, fear, and sadness. Both single-channel and multichannel approaches are applied, with a preference for the first one. For multimodal recognition, early fusion was the most frequently applied. SVM and neural networks were the most popular for building classifiers. Qualitative analysis revealed important clues on participant group construction and the most common combinations of modalities and methods. All channels are reported to be prone to some disturbance, and as a result, information on a specific symptoms of emotions might be temporarily or permanently unavailable. The challenges of proper stimuli, labelling methods, and the creation of open datasets were also identified.

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Autism Spectrum Disorder Detection Using Transfer Learning with VGG 19, Inception V3 and DenseNet 201

Rabbi

Zohra

Hossain

et al. 2023

Communications in Computer and Information Science

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Emotion Detection of Autistic Children Using Image Processing

Cited by 22 publications

References 5 publications

ROSUS 2023 - Računalniška obdelava slik in njena uporaba v Sloveniji 2023: Zbornik 17. strokovne konference

ROSUS 2023 - Računalniška obdelava slik in njena uporaba v Sloveniji 2023: Zbornik 17. strokovne konference

Automatic Emotion Recognition in Children with Autism: A Systematic Literature Review

Autism Spectrum Disorder Detection Using Transfer Learning with VGG 19, Inception V3 and DenseNet 201

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