2020
DOI: 10.1007/978-981-15-3607-6_27
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How Can a Robot Calculate the Level of Visual Focus of Human’s Attention

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Cited by 15 publications
(9 citation statements)
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“…In our earlier study [4], we have established a human-robot interaction system to detect the visual focus of attention (VFOA) based on human attention (in case of both reading and browsing purposes). The system detects the person's current task (attention) and estimates the level by detecting the head and estimating the eye region, especially detected iris center within the eye area (gaze pattern calculation).…”
Section: Literature Reviewmentioning
confidence: 99%
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“…In our earlier study [4], we have established a human-robot interaction system to detect the visual focus of attention (VFOA) based on human attention (in case of both reading and browsing purposes). The system detects the person's current task (attention) and estimates the level by detecting the head and estimating the eye region, especially detected iris center within the eye area (gaze pattern calculation).…”
Section: Literature Reviewmentioning
confidence: 99%
“…Then these blocks were normalised and used for training a support vector machine classifier was used to be trained. The 'Dlib' [4], a python library is used for this purpose, which is capable of calculating HOG-based features. In this work, the shape68 predictor [4] has been implemented to match the bounding box coordinates from the detection algorithm.…”
Section: Detection and Segmentation 1) Pupil Position Detectionmentioning
confidence: 99%
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“…In [12], Nazil Perveen described various types of template matching techniques and emphasized on the applications of those techniques. Haar Cascade Classifier was used to identify human head by considering head image as an object in [13].…”
Section: Literature Reviewmentioning
confidence: 99%
“…There are some recent works in detecting eye gaze too. In 2019, Partha Chakraborty analyzed the gaze pattern to calculate the level of visual focus of human's attention [14].…”
Section: Related Workmentioning
confidence: 99%