A fear detection method based on palpebral fissure
Human emotions, such as smiling or laughing, can be expressed in various forms through the face whenever there are stimuli. These changing faces can reflect the emotional states that are used to identify a normal or an abnormal behaviour. This research aims to study the patterns in human faces and i...
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doaj-d790dc7e2bbe41edb098f432a6ace7632021-09-23T04:36:33ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782021-10-0133810301039A fear detection method based on palpebral fissureRawinan Praditsangthong0Bhattarasiri Slakkham1Pattarasinee Bhattarakosol2Department of Mathematics and Computer Science, Chulalongkorn University, ThailandDepartment of Mathematics and Computer Science, Chulalongkorn University, ThailandCorresponding author.; Department of Mathematics and Computer Science, Chulalongkorn University, ThailandHuman emotions, such as smiling or laughing, can be expressed in various forms through the face whenever there are stimuli. These changing faces can reflect the emotional states that are used to identify a normal or an abnormal behaviour. This research aims to study the patterns in human faces and identify the areas of interest (AOI), which is called Facial Landmark Detection (FLD). The investigation of the external elements of eyes is performed, and it consists of the interpalpebral fissure (IPF), the palpebral fissure length (PFL), and the palpebral fissure region (PFR). These elements are applied to classify the emotional differences between neutral and fearful emotions. A method for emotional classification was designed according to the changing values of the IPF, PFL, and PFR. An ID3 algorithm was used to classify the emotions. Three hundred sixty images were derived from horror-thriller-murder movies based on IMDb. This data set was utilized to generate the proposed pattern. This pattern was used to classify the emotions using a decision tree technique that led to the development of an emotional classification model. The accuracy of the emotional classification model between neutral and fearful emotions was 92.50%, thus proving that the proposed model is efficient.http://www.sciencedirect.com/science/article/pii/S1319157818313454Interpalpebral fissurePalpebral fissure regionEyeEmotionDecision treeClassification |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Rawinan Praditsangthong Bhattarasiri Slakkham Pattarasinee Bhattarakosol |
spellingShingle |
Rawinan Praditsangthong Bhattarasiri Slakkham Pattarasinee Bhattarakosol A fear detection method based on palpebral fissure Journal of King Saud University: Computer and Information Sciences Interpalpebral fissure Palpebral fissure region Eye Emotion Decision tree Classification |
author_facet |
Rawinan Praditsangthong Bhattarasiri Slakkham Pattarasinee Bhattarakosol |
author_sort |
Rawinan Praditsangthong |
title |
A fear detection method based on palpebral fissure |
title_short |
A fear detection method based on palpebral fissure |
title_full |
A fear detection method based on palpebral fissure |
title_fullStr |
A fear detection method based on palpebral fissure |
title_full_unstemmed |
A fear detection method based on palpebral fissure |
title_sort |
fear detection method based on palpebral fissure |
publisher |
Elsevier |
series |
Journal of King Saud University: Computer and Information Sciences |
issn |
1319-1578 |
publishDate |
2021-10-01 |
description |
Human emotions, such as smiling or laughing, can be expressed in various forms through the face whenever there are stimuli. These changing faces can reflect the emotional states that are used to identify a normal or an abnormal behaviour. This research aims to study the patterns in human faces and identify the areas of interest (AOI), which is called Facial Landmark Detection (FLD). The investigation of the external elements of eyes is performed, and it consists of the interpalpebral fissure (IPF), the palpebral fissure length (PFL), and the palpebral fissure region (PFR). These elements are applied to classify the emotional differences between neutral and fearful emotions. A method for emotional classification was designed according to the changing values of the IPF, PFL, and PFR. An ID3 algorithm was used to classify the emotions. Three hundred sixty images were derived from horror-thriller-murder movies based on IMDb. This data set was utilized to generate the proposed pattern. This pattern was used to classify the emotions using a decision tree technique that led to the development of an emotional classification model. The accuracy of the emotional classification model between neutral and fearful emotions was 92.50%, thus proving that the proposed model is efficient. |
topic |
Interpalpebral fissure Palpebral fissure region Eye Emotion Decision tree Classification |
url |
http://www.sciencedirect.com/science/article/pii/S1319157818313454 |
work_keys_str_mv |
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