2D-human face recognition using SIFT and SURF descriptors of face’s feature regions
Face recognition is the process of identifying people through facial images. It has become vital for security and surveillance applications and required everywhere including institutions, organizations, offices, and social places. There are a number of challenges faced in face recognition which incl...
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| Published in | The Visual computer Vol. 37; no. 3; pp. 447 - 456 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.03.2021
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Face recognition is the process of identifying people through facial images. It has become vital for security and surveillance applications and required everywhere including institutions, organizations, offices, and social places. There are a number of challenges faced in face recognition which includes face pose, age, gender, illumination, and other variable condition. Another challenge is that the database size for these applications is usually small. So, training and recognition become difficult. Face recognition methods can be divided into two major categories, appearance-based method and feature-based method. In this paper, the authors have presented the feature-based method for 2D face images. speeded up robust features (SURF) and scale-invariant feature transform (SIFT) are used for feature extraction. Five public datasets, namely Yale2B, Face 94, M2VTS, ORL, and FERET, are used for experimental work. Various combinations of SIFT and SURF features with two classification techniques, namely decision tree and random forest, have experimented in this work. A maximum recognition accuracy of 99.7% has been reported by the authors with a combination of SIFT (64-components) and SURF (32-components). |
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| AbstractList | Face recognition is the process of identifying people through facial images. It has become vital for security and surveillance applications and required everywhere including institutions, organizations, offices, and social places. There are a number of challenges faced in face recognition which includes face pose, age, gender, illumination, and other variable condition. Another challenge is that the database size for these applications is usually small. So, training and recognition become difficult. Face recognition methods can be divided into two major categories, appearance-based method and feature-based method. In this paper, the authors have presented the feature-based method for 2D face images. speeded up robust features (SURF) and scale-invariant feature transform (SIFT) are used for feature extraction. Five public datasets, namely Yale2B, Face 94, M2VTS, ORL, and FERET, are used for experimental work. Various combinations of SIFT and SURF features with two classification techniques, namely decision tree and random forest, have experimented in this work. A maximum recognition accuracy of 99.7% has been reported by the authors with a combination of SIFT (64-components) and SURF (32-components). |
| Author | Gupta, Surbhi Kumar, Munish Thakur, Kutub |
| Author_xml | – sequence: 1 givenname: Surbhi surname: Gupta fullname: Gupta, Surbhi organization: Department of Computer Science and Engineering, Gokaraju Rangaraju Institute of Engineering and Technology – sequence: 2 givenname: Kutub surname: Thakur fullname: Thakur, Kutub organization: Department of Professional Security Studies, Cyber Security, New Jersey City University – sequence: 3 givenname: Munish surname: Kumar fullname: Kumar, Munish email: munishcse@gmail.com organization: Department of Computational Sciences, Maharaja Ranjit Singh Punjab Technical University |
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| Cites_doi | 10.1371/journal.pone.0086041 10.1016/j.tics.2017.09.007 10.1007/s00371-017-1428-z 10.1109/TPAMI.2005.55 10.1016/j.neucom.2015.08.128 10.1016/j.asoc.2015.10.039 10.1016/j.patcog.2016.12.008 10.1109/TPAMI.2017.2781233 10.1007/s00371-013-0861-x 10.1080/09500340.2017.1380854 10.3390/s18072080 10.2174/1874444301507011721 10.1109/34.927464 10.1016/j.procs.2015.10.068 10.1109/34.598228 10.1007/s00521-018-3677-9 10.1109/34.879790 10.1109/TIP.2017.2713940 10.1109/TIFS.2016.2515505 10.1016/j.jvcir.2019.01.013 10.1016/j.inffus.2014.06.001 10.1007/978-3-319-14442-9_59 10.1007/978-3-319-22180-9_31 10.5220/0005314404470454 10.1109/WACV.2014.6835990 10.1109/CVPRW.2016.23 10.1109/ICICS.2013.6782777 10.1007/BFb0016021 10.1007/978-3-319-46478-7_31 10.1007/978-3-319-15554-8_73 10.1109/ICMEAE.2014.16 |
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| Copyright | Springer-Verlag GmbH Germany, part of Springer Nature 2020 Springer-Verlag GmbH Germany, part of Springer Nature 2020. |
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| References | He, Yan, Hu, Niyogi, Zhang (CR11) 2005; 27 CR19 Ke, Peng, Liu, Li, Pei (CR14) 2018; 65 Liu, Shen, Gui, Wang, Li, Yan, Wang (CR20) 2016; 204 CR15 CR37 Phillips, Moon, Rizvi, Rauss (CR23) 2000; 22 Georghiades, Belhumeur, Kriegman (CR7) 2001; 23 CR34 Yan, Li, Wang, Zhao, Liu, Chen (CR36) 2014; 9 CR10 CR32 Lu, Wang, Zhou (CR21) 2017; 26 Guntupalli, Gobbini (CR8) 2017; 21 Vinay, Hebbar, Shekhar, Murthy, Natarajan (CR29) 2015; 70 Karczmarek, Kiersztyn, Pedrycz, Dolecki (CR13) 2017; 65 Li, Qiu, Wen, Xie, Wen (CR18) 2018; 18 Du, Su, Cai (CR6) 2009; 7496 Abdurrahim, Samad, Huddin (CR1) 2018; 34 Chhabra, Garg, Kumar (CR5) 2018 Ranjan, Patel, Chellappa (CR25) 2019; 41 CR4 Vinay, Rao, Shekhar, Kumar, Murthy, Natarajan (CR30) 2015; 70 CR3 CR28 CR9 CR27 CR26 CR24 Werghi, Tortorici, Berretti, Del (CR35) 2016; 11 Kotropoulos, Pitas (CR16) 1997; 4 Wang (CR31) 2019; 60 Naik, Panda (CR22) 2016; 38 Huang, Li, Wang, Zhang (CR12) 2015; 22 Li, Zhou, Su (CR17) 2015; 7 Wang, Miao, Wu, Wan, Tang (CR33) 2014; 30 Belhumeur, Hespanha, Kriegman (CR2) 1997; 19 P Chhabra (1814_CR5) 2018 1814_CR24 BD Liu (1814_CR20) 2016; 204 1814_CR28 A Vinay (1814_CR30) 2015; 70 N Werghi (1814_CR35) 2016; 11 X He (1814_CR11) 2005; 27 1814_CR26 1814_CR27 PN Belhumeur (1814_CR2) 1997; 19 J Lu (1814_CR21) 2017; 26 ZH Huang (1814_CR12) 2015; 22 MK Naik (1814_CR22) 2016; 38 P Karczmarek (1814_CR13) 2017; 65 JS Guntupalli (1814_CR8) 2017; 21 J Ke (1814_CR14) 2018; 65 1814_CR10 1814_CR32 Z Wang (1814_CR33) 2014; 30 PJ Phillips (1814_CR23) 2000; 22 D Wang (1814_CR31) 2019; 60 1814_CR34 R Ranjan (1814_CR25) 2019; 41 G Du (1814_CR6) 2009; 7496 A Georghiades (1814_CR7) 2001; 23 1814_CR9 1814_CR15 1814_CR37 SH Abdurrahim (1814_CR1) 2018; 34 A Vinay (1814_CR29) 2015; 70 1814_CR4 C Kotropoulos (1814_CR16) 1997; 4 1814_CR19 WJ Yan (1814_CR36) 2014; 9 J Li (1814_CR18) 2018; 18 1814_CR3 G Li (1814_CR17) 2015; 7 |
| References_xml | – volume: 9 start-page: e86041 year: 2014 ident: CR36 article-title: CASME II: an improved spontaneous micro-expression database and the baseline evaluation publication-title: PLoS ONE doi: 10.1371/journal.pone.0086041 – volume: 21 start-page: 915 issue: 12 year: 2017 end-page: 916 ident: CR8 article-title: Reading faces: from features to recognition publication-title: Trends Cognit. Sci. doi: 10.1016/j.tics.2017.09.007 – volume: 34 start-page: 1617 issue: 11 year: 2018 end-page: 1630 ident: CR1 article-title: Review on the effects of age, gender, and race demographics on automatic face recognition publication-title: The Visual Computer doi: 10.1007/s00371-017-1428-z – ident: CR4 – ident: CR37 – volume: 27 start-page: 328 issue: 3 year: 2005 end-page: 340 ident: CR11 article-title: Face recognition using laplacianfaces publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2005.55 – volume: 204 start-page: 198 year: 2016 end-page: 210 ident: CR20 article-title: Face recognition using class specific dictionary learning for sparse representation and collaborative representation publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.08.128 – ident: CR10 – volume: 4 start-page: 2537 year: 1997 end-page: 2540 ident: CR16 article-title: Rule-based face detection in frontal views publication-title: IEEE Int. Conf. Acoustics Speech Signal Process. – volume: 38 start-page: 661 year: 2016 end-page: 675 ident: CR22 article-title: A novel adaptive cuckoo search algorithm for intrinsic discriminant analysis based face recognition publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2015.10.039 – volume: 65 start-page: 26 year: 2017 end-page: 34 ident: CR13 article-title: An application of chain code-based local descriptor and its extension to face recognition publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2016.12.008 – volume: 41 start-page: 121 issue: 1 year: 2019 end-page: 135 ident: CR25 article-title: Hyperface: a deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2017.2781233 – volume: 30 start-page: 359 issue: 4 year: 2014 end-page: 386 ident: CR33 article-title: Low-resolution face recognition: a review publication-title: Vis. Comput. doi: 10.1007/s00371-013-0861-x – volume: 65 start-page: 367 issue: 4 year: 2018 end-page: 380 ident: CR14 article-title: Face recognition based on symmetrical virtual image and original training image publication-title: J. Mod. Opt. doi: 10.1080/09500340.2017.1380854 – ident: CR27 – volume: 7496 start-page: 749628 year: 2009 ident: CR6 article-title: Face recognition using SURF features publication-title: Pattern Recognit. Comput. Vis. – volume: 18 start-page: E2080 issue: 7 year: 2018 ident: CR18 article-title: Robust face recognition using the deep C2D-CNN model based on decision-level fusion publication-title: Sensors doi: 10.3390/s18072080 – volume: 7 start-page: 1721 issue: 1 year: 2015 end-page: 1728 ident: CR17 article-title: Face recognition algorithm using two dimensional locality preserving projection in discrete wavelet domain publication-title: Open Autom. Control Syst. J. doi: 10.2174/1874444301507011721 – ident: CR19 – volume: 23 start-page: 643 issue: 6 year: 2001 end-page: 660 ident: CR7 article-title: From few to many: illumination cone models for face recognition under variable lighting and pose publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/34.927464 – volume: 70 start-page: 174 year: 2015 end-page: 184 ident: CR30 article-title: Feature extraction using ORB-RANSAC for face recognition publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2015.10.068 – ident: CR3 – ident: CR15 – volume: 19 start-page: 711 issue: 7 year: 1997 end-page: 720 ident: CR2 article-title: Eigenfaces vs. Fisherfaces: recognition using class specific linear projection publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/34.598228 – year: 2018 ident: CR5 article-title: Content-based image retrieval system using ORB and SIFT features publication-title: Neural Comput. Appl. doi: 10.1007/s00521-018-3677-9 – volume: 22 start-page: 1090 issue: 10 year: 2000 end-page: 1104 ident: CR23 article-title: The FERET evaluation methodology for face-recognition algorithms publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/34.879790 – ident: CR9 – volume: 26 start-page: 4042 issue: 8 year: 2017 end-page: 4054 ident: CR21 article-title: Simultaneous feature and dictionary learning for image set based face recognition publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2017.2713940 – ident: CR32 – volume: 70 start-page: 185 year: 2015 end-page: 197 ident: CR29 article-title: Two novel detector-descriptor based approaches for face recognition using sift and surf publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2015.10.068 – ident: CR34 – volume: 11 start-page: 964 issue: 5 year: 2016 end-page: 979 ident: CR35 article-title: Boosting 3D LBP-based face recognition by fusing shape and texture descriptors on the mesh publication-title: IEEE Trans. Inf. Forensics Secur. doi: 10.1109/TIFS.2016.2515505 – volume: 60 start-page: 116 year: 2019 end-page: 122 ident: CR31 article-title: Effect of subject’s age and gender on face recognition results publication-title: J. Vis. Commun. Image Represent. doi: 10.1016/j.jvcir.2019.01.013 – volume: 22 start-page: 95 year: 2015 end-page: 104 ident: CR12 article-title: Face recognition based on pixel-level and feature-level fusion of the top-level’s wavelet sub-bands publication-title: Inf. Fusion doi: 10.1016/j.inffus.2014.06.001 – ident: CR28 – ident: CR26 – ident: CR24 – ident: 1814_CR27 doi: 10.1007/978-3-319-14442-9_59 – volume: 38 start-page: 661 year: 2016 ident: 1814_CR22 publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2015.10.039 – volume: 9 start-page: e86041 year: 2014 ident: 1814_CR36 publication-title: PLoS ONE doi: 10.1371/journal.pone.0086041 – volume: 204 start-page: 198 year: 2016 ident: 1814_CR20 publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.08.128 – volume: 19 start-page: 711 issue: 7 year: 1997 ident: 1814_CR2 publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/34.598228 – ident: 1814_CR3 doi: 10.1007/978-3-319-22180-9_31 – volume: 23 start-page: 643 issue: 6 year: 2001 ident: 1814_CR7 publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/34.927464 – ident: 1814_CR15 doi: 10.5220/0005314404470454 – ident: 1814_CR37 doi: 10.1109/WACV.2014.6835990 – volume: 70 start-page: 174 year: 2015 ident: 1814_CR30 publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2015.10.068 – volume: 34 start-page: 1617 issue: 11 year: 2018 ident: 1814_CR1 publication-title: The Visual Computer doi: 10.1007/s00371-017-1428-z – volume: 11 start-page: 964 issue: 5 year: 2016 ident: 1814_CR35 publication-title: IEEE Trans. Inf. Forensics Secur. doi: 10.1109/TIFS.2016.2515505 – volume: 7 start-page: 1721 issue: 1 year: 2015 ident: 1814_CR17 publication-title: Open Autom. Control Syst. J. doi: 10.2174/1874444301507011721 – volume: 18 start-page: E2080 issue: 7 year: 2018 ident: 1814_CR18 publication-title: Sensors doi: 10.3390/s18072080 – volume: 26 start-page: 4042 issue: 8 year: 2017 ident: 1814_CR21 publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2017.2713940 – ident: 1814_CR9 doi: 10.1109/CVPRW.2016.23 – volume: 65 start-page: 367 issue: 4 year: 2018 ident: 1814_CR14 publication-title: J. Mod. Opt. doi: 10.1080/09500340.2017.1380854 – volume: 7496 start-page: 749628 year: 2009 ident: 1814_CR6 publication-title: Pattern Recognit. Comput. Vis. – volume: 27 start-page: 328 issue: 3 year: 2005 ident: 1814_CR11 publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2005.55 – ident: 1814_CR19 doi: 10.1109/ICICS.2013.6782777 – volume: 30 start-page: 359 issue: 4 year: 2014 ident: 1814_CR33 publication-title: Vis. Comput. doi: 10.1007/s00371-013-0861-x – volume: 60 start-page: 116 year: 2019 ident: 1814_CR31 publication-title: J. Vis. Commun. Image Represent. doi: 10.1016/j.jvcir.2019.01.013 – volume: 22 start-page: 95 year: 2015 ident: 1814_CR12 publication-title: Inf. Fusion doi: 10.1016/j.inffus.2014.06.001 – volume: 22 start-page: 1090 issue: 10 year: 2000 ident: 1814_CR23 publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/34.879790 – ident: 1814_CR28 – volume: 70 start-page: 185 year: 2015 ident: 1814_CR29 publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2015.10.068 – volume: 65 start-page: 26 year: 2017 ident: 1814_CR13 publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2016.12.008 – ident: 1814_CR24 doi: 10.1007/BFb0016021 – ident: 1814_CR34 doi: 10.1007/978-3-319-46478-7_31 – volume: 21 start-page: 915 issue: 12 year: 2017 ident: 1814_CR8 publication-title: Trends Cognit. Sci. doi: 10.1016/j.tics.2017.09.007 – year: 2018 ident: 1814_CR5 publication-title: Neural Comput. Appl. doi: 10.1007/s00521-018-3677-9 – volume: 41 start-page: 121 issue: 1 year: 2019 ident: 1814_CR25 publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2017.2781233 – ident: 1814_CR26 – ident: 1814_CR32 doi: 10.1007/978-3-319-15554-8_73 – ident: 1814_CR10 – ident: 1814_CR4 doi: 10.1109/ICMEAE.2014.16 – volume: 4 start-page: 2537 year: 1997 ident: 1814_CR16 publication-title: IEEE Int. Conf. Acoustics Speech Signal Process. |
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| SubjectTerms | Accuracy Algorithms Artificial Intelligence Automation Computer Graphics Computer Science Decision trees Dictionaries Face recognition Facial recognition technology Feature extraction Feature recognition Identification Image Processing and Computer Vision Image retrieval Localization Neural networks Original Article Principal components analysis Wavelet transforms |
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| Title | 2D-human face recognition using SIFT and SURF descriptors of face’s feature regions |
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