Responsive human-computer interaction model based on recognition of facial landmarks using machine learning algorithms
Abstract This paper is addressed on the idea of building up a model to control computer systems by utilizing facial landmarks like eyes, nose and head gestures. The face recognition systems mainly detect and recognize eyes, nose and head gestures to control the movement of the mouse cursor in order...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Bisen, Dhananjay [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 81(2022), 13 vom: 08. März, Seite 18011-18031 |
|---|---|
| Übergeordnetes Werk: |
volume:81 ; year:2022 ; number:13 ; day:08 ; month:03 ; pages:18011-18031 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11042-022-12775-6 |
|---|
| Katalog-ID: |
OLC2078602647 |
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| 520 | |a Abstract This paper is addressed on the idea of building up a model to control computer systems by utilizing facial landmarks like eyes, nose and head gestures. The face recognition systems mainly detect and recognize eyes, nose and head gestures to control the movement of the mouse cursor in order to operate computer system in real time. This paper proposes the facial landmarks based human-computer interaction model in which histogram of oriented gradients (HOG) has been taken for global facial feature identification and extraction that is considered as HOG descriptors. Furthermore, pre-trained linear SVM classifier gets extracted features to detect whether it is a human face or not, including use of pyramid based images and sliding window algorithm. Moreover pre-trained ensemble of Regression Trees algorithm is applied to recognize facial landmarks such as eyes, eyebrows, nose, mouth, and jawline. The main purpose is to effectively utilize facial landmarks and allow the user to perform activities mapped to explicit eye blinks, nose and head motions using PC webcam. In this model, eye blinks has been detected through estimated value of eye aspect ratio (EAR) and newly proposed β parameter. Accordingly classification report has generated for both estimation and analysed best results for β parameter in terms of accuracy with 98.33%, precision with 100%, recall with 98.33% and F1 score with 99.16% under good lighting conditions. | ||
| 650 | 4 | |a Human computer interaction | |
| 650 | 4 | |a Gesture recognition | |
| 650 | 4 | |a Facial recognition | |
| 650 | 4 | |a DLib | |
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| 700 | 1 | |a Rajpoot, Narendra |4 aut | |
| 700 | 1 | |a Maurya, Praphull |4 aut | |
| 700 | 1 | |a Uttam, Atul Kr. |4 aut | |
| 700 | 1 | |a Arjaria, Siddhartha kr. |4 aut | |
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10.1007/s11042-022-12775-6 doi (DE-627)OLC2078602647 (DE-He213)s11042-022-12775-6-p DE-627 ger DE-627 rakwb eng 070 004 VZ Bisen, Dhananjay verfasserin aut Responsive human-computer interaction model based on recognition of facial landmarks using machine learning algorithms 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract This paper is addressed on the idea of building up a model to control computer systems by utilizing facial landmarks like eyes, nose and head gestures. The face recognition systems mainly detect and recognize eyes, nose and head gestures to control the movement of the mouse cursor in order to operate computer system in real time. This paper proposes the facial landmarks based human-computer interaction model in which histogram of oriented gradients (HOG) has been taken for global facial feature identification and extraction that is considered as HOG descriptors. Furthermore, pre-trained linear SVM classifier gets extracted features to detect whether it is a human face or not, including use of pyramid based images and sliding window algorithm. Moreover pre-trained ensemble of Regression Trees algorithm is applied to recognize facial landmarks such as eyes, eyebrows, nose, mouth, and jawline. The main purpose is to effectively utilize facial landmarks and allow the user to perform activities mapped to explicit eye blinks, nose and head motions using PC webcam. In this model, eye blinks has been detected through estimated value of eye aspect ratio (EAR) and newly proposed β parameter. Accordingly classification report has generated for both estimation and analysed best results for β parameter in terms of accuracy with 98.33%, precision with 100%, recall with 98.33% and F1 score with 99.16% under good lighting conditions. Human computer interaction Gesture recognition Facial recognition DLib Eye blink detection HOG EAR Shukla, Rishabh aut Rajpoot, Narendra aut Maurya, Praphull aut Uttam, Atul Kr. aut Arjaria, Siddhartha kr. aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2022), 13 vom: 08. März, Seite 18011-18031 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2022 number:13 day:08 month:03 pages:18011-18031 https://doi.org/10.1007/s11042-022-12775-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2022 13 08 03 18011-18031 |
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10.1007/s11042-022-12775-6 doi (DE-627)OLC2078602647 (DE-He213)s11042-022-12775-6-p DE-627 ger DE-627 rakwb eng 070 004 VZ Bisen, Dhananjay verfasserin aut Responsive human-computer interaction model based on recognition of facial landmarks using machine learning algorithms 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract This paper is addressed on the idea of building up a model to control computer systems by utilizing facial landmarks like eyes, nose and head gestures. The face recognition systems mainly detect and recognize eyes, nose and head gestures to control the movement of the mouse cursor in order to operate computer system in real time. This paper proposes the facial landmarks based human-computer interaction model in which histogram of oriented gradients (HOG) has been taken for global facial feature identification and extraction that is considered as HOG descriptors. Furthermore, pre-trained linear SVM classifier gets extracted features to detect whether it is a human face or not, including use of pyramid based images and sliding window algorithm. Moreover pre-trained ensemble of Regression Trees algorithm is applied to recognize facial landmarks such as eyes, eyebrows, nose, mouth, and jawline. The main purpose is to effectively utilize facial landmarks and allow the user to perform activities mapped to explicit eye blinks, nose and head motions using PC webcam. In this model, eye blinks has been detected through estimated value of eye aspect ratio (EAR) and newly proposed β parameter. Accordingly classification report has generated for both estimation and analysed best results for β parameter in terms of accuracy with 98.33%, precision with 100%, recall with 98.33% and F1 score with 99.16% under good lighting conditions. Human computer interaction Gesture recognition Facial recognition DLib Eye blink detection HOG EAR Shukla, Rishabh aut Rajpoot, Narendra aut Maurya, Praphull aut Uttam, Atul Kr. aut Arjaria, Siddhartha kr. aut Enthalten in Multimedia tools and applications Springer US, 1995 81(2022), 13 vom: 08. März, Seite 18011-18031 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:81 year:2022 number:13 day:08 month:03 pages:18011-18031 https://doi.org/10.1007/s11042-022-12775-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 81 2022 13 08 03 18011-18031 |
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Responsive human-computer interaction model based on recognition of facial landmarks using machine learning algorithms |
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Responsive human-computer interaction model based on recognition of facial landmarks using machine learning algorithms |
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Bisen, Dhananjay |
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Bisen, Dhananjay Shukla, Rishabh Rajpoot, Narendra Maurya, Praphull Uttam, Atul Kr. Arjaria, Siddhartha kr. |
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responsive human-computer interaction model based on recognition of facial landmarks using machine learning algorithms |
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Responsive human-computer interaction model based on recognition of facial landmarks using machine learning algorithms |
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Abstract This paper is addressed on the idea of building up a model to control computer systems by utilizing facial landmarks like eyes, nose and head gestures. The face recognition systems mainly detect and recognize eyes, nose and head gestures to control the movement of the mouse cursor in order to operate computer system in real time. This paper proposes the facial landmarks based human-computer interaction model in which histogram of oriented gradients (HOG) has been taken for global facial feature identification and extraction that is considered as HOG descriptors. Furthermore, pre-trained linear SVM classifier gets extracted features to detect whether it is a human face or not, including use of pyramid based images and sliding window algorithm. Moreover pre-trained ensemble of Regression Trees algorithm is applied to recognize facial landmarks such as eyes, eyebrows, nose, mouth, and jawline. The main purpose is to effectively utilize facial landmarks and allow the user to perform activities mapped to explicit eye blinks, nose and head motions using PC webcam. In this model, eye blinks has been detected through estimated value of eye aspect ratio (EAR) and newly proposed β parameter. Accordingly classification report has generated for both estimation and analysed best results for β parameter in terms of accuracy with 98.33%, precision with 100%, recall with 98.33% and F1 score with 99.16% under good lighting conditions. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
| abstractGer |
Abstract This paper is addressed on the idea of building up a model to control computer systems by utilizing facial landmarks like eyes, nose and head gestures. The face recognition systems mainly detect and recognize eyes, nose and head gestures to control the movement of the mouse cursor in order to operate computer system in real time. This paper proposes the facial landmarks based human-computer interaction model in which histogram of oriented gradients (HOG) has been taken for global facial feature identification and extraction that is considered as HOG descriptors. Furthermore, pre-trained linear SVM classifier gets extracted features to detect whether it is a human face or not, including use of pyramid based images and sliding window algorithm. Moreover pre-trained ensemble of Regression Trees algorithm is applied to recognize facial landmarks such as eyes, eyebrows, nose, mouth, and jawline. The main purpose is to effectively utilize facial landmarks and allow the user to perform activities mapped to explicit eye blinks, nose and head motions using PC webcam. In this model, eye blinks has been detected through estimated value of eye aspect ratio (EAR) and newly proposed β parameter. Accordingly classification report has generated for both estimation and analysed best results for β parameter in terms of accuracy with 98.33%, precision with 100%, recall with 98.33% and F1 score with 99.16% under good lighting conditions. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
| abstract_unstemmed |
Abstract This paper is addressed on the idea of building up a model to control computer systems by utilizing facial landmarks like eyes, nose and head gestures. The face recognition systems mainly detect and recognize eyes, nose and head gestures to control the movement of the mouse cursor in order to operate computer system in real time. This paper proposes the facial landmarks based human-computer interaction model in which histogram of oriented gradients (HOG) has been taken for global facial feature identification and extraction that is considered as HOG descriptors. Furthermore, pre-trained linear SVM classifier gets extracted features to detect whether it is a human face or not, including use of pyramid based images and sliding window algorithm. Moreover pre-trained ensemble of Regression Trees algorithm is applied to recognize facial landmarks such as eyes, eyebrows, nose, mouth, and jawline. The main purpose is to effectively utilize facial landmarks and allow the user to perform activities mapped to explicit eye blinks, nose and head motions using PC webcam. In this model, eye blinks has been detected through estimated value of eye aspect ratio (EAR) and newly proposed β parameter. Accordingly classification report has generated for both estimation and analysed best results for β parameter in terms of accuracy with 98.33%, precision with 100%, recall with 98.33% and F1 score with 99.16% under good lighting conditions. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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Responsive human-computer interaction model based on recognition of facial landmarks using machine learning algorithms |
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