Mean of Correlation Method for Optimization of Affective States Detection in Children

Rusli, Nazreen; Sidek, Shahrul Na’im; Yusof, Hazlina Md; Ishak, Nor Izzati

doi:10.1109/access.2018.2878144

Cited by 9 publications

(5 citation statements)

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“…The selection of supraorbital and periorbital regions as the ROIs in the study stemmed from the fact that the two ROIs were found to be highly independent and significant in the affective state model analysis [38]. A matching template technique was implemented to automatically locate the ROIs for a complete series of thermal images for a subject.…”

Section: A Segmentation Of Region Of Interest (Roi)mentioning

confidence: 99%

Implementation of Wavelet Analysis on Thermal Images for Affective States Recognition of Children With Autism Spectrum Disorder

et al. 2020

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Children with Autism Spectrum Disorder are identified as a group of people who has difficulties in socio-emotional interaction. Most of them lack the proper context in producing social response through facial expression and speech. Since emotion is the key for effective social interaction, it is justifiably vital for them to comprehend the correct emotion expressions and recognitions. Emotion is a type of affective states and can be detected through physical reaction and physiological signals. In general, recognition of affective states from physical reaction such as facial expression and speech for autistic children is often unpredictable. Hence, an alternative method of identifying the affective states through physiological signals is proposed. Though considered non-invasive, most of the current recognition methods require sensors to be patched on to the skin body to measure the signals. This would most likely cause discomfort to the children and mask their "true" affective states. The study proposed the use of thermal imaging modality as a passive medium to analyze the physiological signals associated with the affective states nonobtrusively. The study hypothesized that, the impact of cutaneous temperature changes due to the pulsating blood flow in the blood vessels at the frontal face area measured from the modality could have a direct impact to the different affective states of autistic children. A structured experimental setup was designed to measure thermal imaging data generated from different affective state expressions induced using different sets of audio-video stimuli. A wavelet-based technique for pattern detection in time series was deployed to spot the changes measured from the region of interest. In the study, the affective state model for typical developing children aged between 5 and 9 years old was used as the baseline to evaluate the performance of the affective state classifier for autistic children. The results from the classifier showed the efficacy of the technique and accorded good performance of classification accuracy at 88% in identifying the affective states of autistic children. The results were momentous in distinguishing basic affective states and the information could provide a more effective response towards improving social-emotion interaction amongst the autistic children. INDEX TERMS Autism, Affective States, Facial Skin Temperatures, Thermoregulation, Thermal Images, Wavelet I. INTRODUCTION F OR decades, numbers of research have been conducted in the field of affective state recognition, using numerous modalities and exploiting various features from the signal generated by the Autonomous Nervous System (ANS). ANS together with hypothalamus, regulates the blood pressure, breathing, pulse and arousal in response to different emotional features. Seeing the big potentials of affective state

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Section: A Segmentation Of Region Of Interest (Roi)mentioning

confidence: 99%

Implementation of Wavelet Analysis on Thermal Images for Affective States Recognition of Children With Autism Spectrum Disorder

et al. 2020

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“…Human emotions that can be evaluated in a contactless manner using non-invasive technique is implemented in [8] that uses thermal signatures from face image from the front. A thermal imager which is a seamless device is utilized in order to detect various emotion labels where it registers heat signatures radiated from subject's face.…”

Section: Sensor and Feature Level Fusion Of Thermal Image And Ecg Sigmentioning

confidence: 99%

“…Texture analysis based on a unique algorithm which extracts feature from GLCM method which detects region where flows dominantly. In this region, first order statistics is computed based on the cues and this is combined with cues of higher order statistics to build an efficient feature vector that can expose important and dominant patterns for different labels of emotion [8].…”

Section: Sensor and Feature Level Fusion Of Thermal Image And Ecg Sigmentioning

confidence: 99%

“…It can be viewed as gray level histogram in two dimensions. Here pixel distance and direction is used to compute occurrence probability between a pair of pixels [8]. The functions based on GLCM categorize the image surface by considering pair of pixels of specific relationship between them in spatial domain and also specified values which is there in GLCM image forms [7].…”

Section: Feature Selectionmentioning

confidence: 99%

“…A ROI is that part of an image that can be used for further processing. Face of the subject is the Region of Interest here which is cropped manually from each thermal image[8].C. Feature Extraction & SelectionECG signal: After applying Empirical Mode Decomposition (EMD) on ECG signal, three Intrinsic Mode Function (IMF) were considered viz IMF1, IMF2, IMF3.…”

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Sensor and Feature Level Fusion of Thermal Image and ECG Signals in Recognizing Human Emotions

2019

IJITEE

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Recent studies on recognition of various emotion labels concentrated on speech signals, text, visual images and anatomical variables. The proposed system combines the features of ECG which are extracted using empirical mode decomposition and features of thermal images which are extracted from Gray Level Co-occurrence Matrix (GLCM) viz energy, contrast, homogeneity and correlation. ECG is acquired from AD8232 module and thermal images from FLUKE TiS20. Data of ECG and thermal images are acquired simultaneously from a subject and database consists of data from 40 subjects in age group of 20 years to 40 years from Hassan, Karnataka, India. Here different labels of emotions have been classified based on K-nearest neighbor decision rule. This system yielded highest accuracy for disgust and lowest for anger using ECG and highest accuracy for disgust and surprise and least for sad.

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Texture Analysis of Blood Vessels in Thermal Image for Affective States Classification on Children

Rusli

Rashidan

Sidek

et al. 2021

Lecture Notes in Mechanical Engineering

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Mean of Correlation Method for Optimization of Affective States Detection in Children

Cited by 9 publications

References 40 publications

Implementation of Wavelet Analysis on Thermal Images for Affective States Recognition of Children With Autism Spectrum Disorder

Implementation of Wavelet Analysis on Thermal Images for Affective States Recognition of Children With Autism Spectrum Disorder

Sensor and Feature Level Fusion of Thermal Image and ECG Signals in Recognizing Human Emotions

Texture Analysis of Blood Vessels in Thermal Image for Affective States Classification on Children

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