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INTELEK Repository
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Online Access
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https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
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2015-12-27 15:28:01
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Restricted Document
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12600
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[1] P. Phillips, P. Flynn, T. Scruggs, K. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min, and W. Worek. 2005. Overview of the Face Recognition Grand Challenge.Computer Vision and Pattern RecognitionIEEEComputer Society Conference.San Diego, CA, USA. 20-25 June 2005.947-954. [2] Colombo, A., C.Cusano, and R.Schettini. 2011. UMB-DB: A Database of Partially Occluded 3D Faces. IEEE International Conference on Computer VisionWorkshops. Barcelona, Spain. 26-13 November2011. 113-2119. [3] R. Gross.2005: Face Databases. Handbookof Face Recognition. New York: Springer. ISBN 0-387-40595-x. [4] A.M. Martinez and R. Benavente. 1998. The AR Face Database. CVC Technical Report. 24. [5] A. Savran, N. Aly ̈uz, H. Dibeklio ̆glu,O. C，eliktutan, B. G ̈okberk, B. Sankur, and L. Akarun. 2008.Biometricsand Identity Management: First European Workshop, BIOID.Springer Berlin Heidelberg.5372:47-56. [6] G.Huang, M Ramesh, T Berg, E Learned-Miller. 2007.Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments. Tech. rep. Technical report, University of Massachusetts, Amherst. [7] Viola, P., and Jones, M.J. 2004. Robust real-time face detection. International Journal of Computer Vision.57(2): 137-154. [8] A.Colombo, C.Cusano, and R.Schettini. 2011. Three-Dimensional Occlusion Detection and Restoration of Partially Occluded Faces.Journal of Mathematical Imaging and Vision. 40(1): 105-119. [9] Colombo,C.,C.Cusano, andR.Schettini. 2007.A 2D+3D Robust Face Recognition System. 14th International Conference on Image Analysis and Processing.Modena, Italy. 10-14 September 2007. 393-398. [10] A. Savran, B. Sankur, M. T. Bilge. 2012. Comparative Evaluation of 3D versus 2D Modality for Automatic Detection of Facial Action Units.Pattern Recognition.45(2): 767-782. [11] Çeliktutan, O., H. Çınar, B. Sankur. 2008. Automatic Facial Feature Extraction Robust Against Facial Expressions and Pose Variations.IEEE InternationalConference on Automatic Face andGesture Recognition, Amsterdam,Holland.17-19 September 2008. [12] Milborrow, S. and F Nicolls. 2008. Locating Facial Features with an Extended ActiveShape Model.European Conferenceon Computer Vision.Marseille, France. 12-18 October 2008.504–513. [13] Urclar, M. and Franc, V. 2015. Open-source implementation of facial landmark detector [online]. From: http://cmp.felk.cvut.cz/~uricamic/flandmark/. [Accessed on 13 January 2015].
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12600 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=12600 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal image/jpeg inches 96 96 norman 72 72 761 1425 2015-12-27 15:28:01 1425x761 6907-01-FH02-FIK-15-04636.jpg UniSZA Private Access A state of the art comparison of databases for facial occlusion Jurnal Teknologi Face recognition continues to be one of the most popular research areas of image processing and computer vision. There are various face databases available to researchers for face detection and recognition. These databases are customized for a particular need of one algorithm. They are range in size, scope, and purpose. Few of these databases from the literature contain face occlusions in several positions of the faces to enable real world applications. In this paper, we present four different occlusion face databases. These are Aleix-Robert (AR),Bosphorus, Labeled Faces in the Wild (LFW), and University of Milano Bicocca Database (UMB) face databases. At each section, the key features of the database are presented with the recording conditions, though not all of them are discussed at the same level of details. Detailed comparisons of the databases were made based on controlled and uncontrolled databases, 2D and 3D databases and also their uniqueness. Comparison was also made with other databases out of the categorization mentioned. The databases are useful for performing a rigorous benchmarking of face detection and recognition algorithms. 77 13 Penerbit UTM Press Penerbit UTM Press 111-117 [1] P. Phillips, P. Flynn, T. Scruggs, K. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min, and W. Worek. 2005. Overview of the Face Recognition Grand Challenge.Computer Vision and Pattern RecognitionIEEEComputer Society Conference.San Diego, CA, USA. 20-25 June 2005.947-954. [2] Colombo, A., C.Cusano, and R.Schettini. 2011. UMB-DB: A Database of Partially Occluded 3D Faces. IEEE International Conference on Computer VisionWorkshops. Barcelona, Spain. 26-13 November2011. 113-2119. [3] R. Gross.2005: Face Databases. Handbookof Face Recognition. New York: Springer. ISBN 0-387-40595-x. [4] A.M. Martinez and R. Benavente. 1998. The AR Face Database. CVC Technical Report. 24. [5] A. Savran, N. Aly ̈uz, H. Dibeklio ̆glu,O. C，eliktutan, B. G ̈okberk, B. Sankur, and L. Akarun. 2008.Biometricsand Identity Management: First European Workshop, BIOID.Springer Berlin Heidelberg.5372:47-56. [6] G.Huang, M Ramesh, T Berg, E Learned-Miller. 2007.Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments. Tech. rep. Technical report, University of Massachusetts, Amherst. [7] Viola, P., and Jones, M.J. 2004. Robust real-time face detection. International Journal of Computer Vision.57(2): 137-154. [8] A.Colombo, C.Cusano, and R.Schettini. 2011. Three-Dimensional Occlusion Detection and Restoration of Partially Occluded Faces.Journal of Mathematical Imaging and Vision. 40(1): 105-119. [9] Colombo,C.,C.Cusano, andR.Schettini. 2007.A 2D+3D Robust Face Recognition System. 14th International Conference on Image Analysis and Processing.Modena, Italy. 10-14 September 2007. 393-398. [10] A. Savran, B. Sankur, M. T. Bilge. 2012. Comparative Evaluation of 3D versus 2D Modality for Automatic Detection of Facial Action Units.Pattern Recognition.45(2): 767-782. [11] Çeliktutan, O., H. Çınar, B. Sankur. 2008. Automatic Facial Feature Extraction Robust Against Facial Expressions and Pose Variations.IEEE InternationalConference on Automatic Face andGesture Recognition, Amsterdam,Holland.17-19 September 2008. [12] Milborrow, S. and F Nicolls. 2008. Locating Facial Features with an Extended ActiveShape Model.European Conferenceon Computer Vision.Marseille, France. 12-18 October 2008.504–513. [13] Urclar, M. and Franc, V. 2015. Open-source implementation of facial landmark detector [online]. From: http://cmp.felk.cvut.cz/~uricamic/flandmark/. [Accessed on 13 January 2015].
|
| spellingShingle |
A state of the art comparison of databases for facial occlusion
|
| summary |
Face recognition continues to be one of the most popular research areas of image processing and computer vision. There are various face databases available to researchers for face detection and recognition. These databases are customized for a particular need of one algorithm. They are range in size, scope, and purpose. Few of these databases from the literature contain face occlusions in several positions of the faces to enable real world applications. In this paper, we present four different occlusion face databases. These are Aleix-Robert (AR),Bosphorus, Labeled Faces in the Wild (LFW), and University of Milano Bicocca Database (UMB) face databases. At each section, the key features of the database are presented with the recording conditions, though not all of them are discussed at the same level of details. Detailed comparisons of the databases were made based on controlled and uncontrolled databases, 2D and 3D databases and also their uniqueness. Comparison was also made with other databases out of the categorization mentioned. The databases are useful for performing a rigorous benchmarking of face detection and recognition algorithms.
|
| title |
A state of the art comparison of databases for facial occlusion
|
| title_full |
A state of the art comparison of databases for facial occlusion
|
| title_fullStr |
A state of the art comparison of databases for facial occlusion
|
| title_full_unstemmed |
A state of the art comparison of databases for facial occlusion
|
| title_short |
A state of the art comparison of databases for facial occlusion
|
| title_sort |
state of the art comparison of databases for facial occlusion
|