Face recognition using local geometrical features - PCA with Euclidean classifier
The goal of this research is to get the minimum features and produce better recognition rates. Before doing the feature selection, we investigate automatic methods for detecting face anchor points with 412 3D-facial points of 60 individuals. There are 7 images per subject including views presenting...
| Main Authors: | , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2008
|
| Online Access: | http://psasir.upm.edu.my/id/eprint/69617/ http://psasir.upm.edu.my/id/eprint/69617/1/Face%20recognition%20using%20local%20geometrical%20features%20-%20PCA%20with%20Euclidean%20classifier.pdf |
| _version_ | 1848856458494476288 |
|---|---|
| author | Khalid, Fatimah Tengku Sembok, Tengku Mohd Omar, Khairuddin |
| author_facet | Khalid, Fatimah Tengku Sembok, Tengku Mohd Omar, Khairuddin |
| author_sort | Khalid, Fatimah |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The goal of this research is to get the minimum features and produce better recognition rates. Before doing the feature selection, we investigate automatic methods for detecting face anchor points with 412 3D-facial points of 60 individuals. There are 7 images per subject including views presenting light rotations and facial expressions. Each images have twelve anchor points which are Right Outer Eye, Right Inner Eye, Left Outer Eye, Left Inner Eye, Upper nose point, Nose Tip, Right Nose Base, Left Nose Base, Right Outer Face, Left Outer Face, Chin, and Upper Face. All the control points are based on the measurement on an absolute scale (mm). After all the control points have been determined, we will extract a relevant set of features. These features are classified in 3 : (1) distance of mass points, (2) angle measurements, and (3) angle measurements. There are fifty-three local geometrical features extracted from 3D points human faces to model the face for face recognition and the discriminating power calculation is to show the valuable feature among all the features. Experiment performed on the GavabDB dataset (412 faces) show that our algorithm achieved 86% of success when respectively the first rank matched. |
| first_indexed | 2025-11-15T11:41:59Z |
| format | Conference or Workshop Item |
| id | upm-69617 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T11:41:59Z |
| publishDate | 2008 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-696172019-07-08T02:03:29Z http://psasir.upm.edu.my/id/eprint/69617/ Face recognition using local geometrical features - PCA with Euclidean classifier Khalid, Fatimah Tengku Sembok, Tengku Mohd Omar, Khairuddin The goal of this research is to get the minimum features and produce better recognition rates. Before doing the feature selection, we investigate automatic methods for detecting face anchor points with 412 3D-facial points of 60 individuals. There are 7 images per subject including views presenting light rotations and facial expressions. Each images have twelve anchor points which are Right Outer Eye, Right Inner Eye, Left Outer Eye, Left Inner Eye, Upper nose point, Nose Tip, Right Nose Base, Left Nose Base, Right Outer Face, Left Outer Face, Chin, and Upper Face. All the control points are based on the measurement on an absolute scale (mm). After all the control points have been determined, we will extract a relevant set of features. These features are classified in 3 : (1) distance of mass points, (2) angle measurements, and (3) angle measurements. There are fifty-three local geometrical features extracted from 3D points human faces to model the face for face recognition and the discriminating power calculation is to show the valuable feature among all the features. Experiment performed on the GavabDB dataset (412 faces) show that our algorithm achieved 86% of success when respectively the first rank matched. IEEE 2008 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/69617/1/Face%20recognition%20using%20local%20geometrical%20features%20-%20PCA%20with%20Euclidean%20classifier.pdf Khalid, Fatimah and Tengku Sembok, Tengku Mohd and Omar, Khairuddin (2008) Face recognition using local geometrical features - PCA with Euclidean classifier. In: 3rd International Symposium on Information Technology (ITSim'08), 26-28 Aug. 2008, Kuala Lumpur, Malaysia. . 10.1109/ITSIM.2008.4631687 |
| spellingShingle | Khalid, Fatimah Tengku Sembok, Tengku Mohd Omar, Khairuddin Face recognition using local geometrical features - PCA with Euclidean classifier |
| title | Face recognition using local geometrical features - PCA with Euclidean classifier |
| title_full | Face recognition using local geometrical features - PCA with Euclidean classifier |
| title_fullStr | Face recognition using local geometrical features - PCA with Euclidean classifier |
| title_full_unstemmed | Face recognition using local geometrical features - PCA with Euclidean classifier |
| title_short | Face recognition using local geometrical features - PCA with Euclidean classifier |
| title_sort | face recognition using local geometrical features - pca with euclidean classifier |
| url | http://psasir.upm.edu.my/id/eprint/69617/ http://psasir.upm.edu.my/id/eprint/69617/ http://psasir.upm.edu.my/id/eprint/69617/1/Face%20recognition%20using%20local%20geometrical%20features%20-%20PCA%20with%20Euclidean%20classifier.pdf |