A novel hybrid biometric software application for facial recognition considering uncontrollable environmental conditions
Human face recognition research in biometric applications is a popular research subject because of its various applications and challenges, including camera type, pose, light or illumination, resolutions, wearing or not wearing glasses, and expressions, among others. This study presents a novel hybr...
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| Published in | Healthcare analytics (New York, N.Y.) Vol. 3; p. 100156 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Inc
01.11.2023
Elsevier |
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| Online Access | Get full text |
| ISSN | 2772-4425 2772-4425 |
| DOI | 10.1016/j.health.2023.100156 |
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| Abstract | Human face recognition research in biometric applications is a popular research subject because of its various applications and challenges, including camera type, pose, light or illumination, resolutions, wearing or not wearing glasses, and expressions, among others. This study presents a novel hybrid biometric software application for facial recognition considering uncontrollable environmental conditions. The proposed system uses two features namely, Laplace of Gaussian filter-based Discrete Wavelet Transform (LGDWT) and Discrete Cosine Transform Compressed based Log Gabor Filter (DCTLGF). The combined LGDWT and DCTLGF features were used by a Multiclass Support Vector Machine (MSVM) classifier to create the desired class label of individual faces. Our work was tested on a face dataset comprising 25 people of 200 face images which are taken using a five-megapixel low-goal web camera and yielded good results in different bounds in contrast to existing techniques.
•This study presents a novel hybrid biometric software application for facial recognition.•The facial features are used by a multiclass support vector machine classifier to create the desired class.•Testing was conducted on a facial database comprising 25 people, each with 200 pictures.•A five-megapixel low-goal web camera yields good results on various boundaries contrasted with existing. techniques. |
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| AbstractList | Human face recognition research in biometric applications is a popular research subject because of its various applications and challenges, including camera type, pose, light or illumination, resolutions, wearing or not wearing glasses, and expressions, among others. This study presents a novel hybrid biometric software application for facial recognition considering uncontrollable environmental conditions. The proposed system uses two features namely, Laplace of Gaussian filter-based Discrete Wavelet Transform (LGDWT) and Discrete Cosine Transform Compressed based Log Gabor Filter (DCTLGF). The combined LGDWT and DCTLGF features were used by a Multiclass Support Vector Machine (MSVM) classifier to create the desired class label of individual faces. Our work was tested on a face dataset comprising 25 people of 200 face images which are taken using a five-megapixel low-goal web camera and yielded good results in different bounds in contrast to existing techniques.
•This study presents a novel hybrid biometric software application for facial recognition.•The facial features are used by a multiclass support vector machine classifier to create the desired class.•Testing was conducted on a facial database comprising 25 people, each with 200 pictures.•A five-megapixel low-goal web camera yields good results on various boundaries contrasted with existing. techniques. Human face recognition research in biometric applications is a popular research subject because of its various applications and challenges, including camera type, pose, light or illumination, resolutions, wearing or not wearing glasses, and expressions, among others. This study presents a novel hybrid biometric software application for facial recognition considering uncontrollable environmental conditions. The proposed system uses two features namely, Laplace of Gaussian filter-based Discrete Wavelet Transform (LGDWT) and Discrete Cosine Transform Compressed based Log Gabor Filter (DCTLGF). The combined LGDWT and DCTLGF features were used by a Multiclass Support Vector Machine (MSVM) classifier to create the desired class label of individual faces. Our work was tested on a face dataset comprising 25 people of 200 face images which are taken using a five-megapixel low-goal web camera and yielded good results in different bounds in contrast to existing techniques. AbstractHuman face recognition research in biometric applications is a popular research subject because of its various applications and challenges, including camera type, pose, light or illumination, resolutions, wearing or not wearing glasses, and expressions, among others. This study presents a novel hybrid biometric software application for facial recognition considering uncontrollable environmental conditions. The proposed system uses two features namely, Laplace of Gaussian filter-based Discrete Wavelet Transform (LGDWT) and Discrete Cosine Transform Compressed based Log Gabor Filter (DCTLGF). The combined LGDWT and DCTLGF features were used by a Multiclass Support Vector Machine (MSVM) classifier to create the desired class label of individual faces. Our work was tested on a face dataset comprising 25 people of 200 face images which are taken using a five-megapixel low-goal web camera and yielded good results in different bounds in contrast to existing techniques. |
| ArticleNumber | 100156 |
| Author | M., Mathivanan H.R., Vijaya Kumar |
| Author_xml | – sequence: 1 givenname: Vijaya Kumar orcidid: 0000-0001-5043-5342 surname: H.R. fullname: H.R., Vijaya Kumar email: vijayec106@gmail.com – sequence: 2 givenname: Mathivanan surname: M. fullname: M., Mathivanan email: mathivananacs@gmail.com |
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| Keywords | Biometric applications Wavelet transform Face recognition Logarithmic gabor Classification |
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| Title | A novel hybrid biometric software application for facial recognition considering uncontrollable environmental conditions |
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