Feature extraction and localisation using scale-invariant feature transform on 2.5D image
anatomical landmarks, which is a vital initial stage for several applications, such as face recognition, facial analysis and synthesis. Locating facial landmarks in images is an important task in image processing and detecting it automatically still remains challenging. The appearance of facial la...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Vaclav Skala - Union Agency
2015
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| Online Access: | http://ir.unimas.my/id/eprint/12107/ http://ir.unimas.my/id/eprint/12107/1/No%2041%20%28abstrak%29.pdf |
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| author | Suk, Ting Pui Jacey-Lynn, Minoi, Terrin, Lim Fradinho Oliveira, João Fyfe Gillies, Duncan |
| author_facet | Suk, Ting Pui Jacey-Lynn, Minoi, Terrin, Lim Fradinho Oliveira, João Fyfe Gillies, Duncan |
| author_sort | Suk, Ting Pui |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| description | anatomical landmarks, which is a vital initial stage for several applications, such as face recognition, facial
analysis and synthesis. Locating facial landmarks in images is an important task in image processing and
detecting it automatically still remains challenging. The appearance of facial landmarks may vary tremendously
due to facial variations. Detecting and extracting landmarks from raw face data is usually done manually by
trained and experienced scientists or clinicians, and the landmarking is a laborious process. Hence, we aim to
develop methods to automate as much as possible the process of landmarking facial features. In this paper, we
present and discuss our new automatic landmarking method on face data using 2.5-dimensional (2.5D) range
images. We applied the Scale-invariant Feature Transform (SIFT) method to extract feature vectors and the
Otsu’s method to obtain a general threshold value for landmark localisation. We have also developed an
interactive tool to ease the visualisation of the overall landmarking process. The interactive visualization tool has
a function which allows users to adjust and explore the threshold values for further analysis, thus enabling one to
determine the threshold values for the detection and extraction of important keypoints or/and regions of facial features that are suitable to be used later automatically with new datasets with the same controlled lighting and pose restrictions. We measured the accuracy of the automatic landmarking versus manual landmarking and found the differences to be marginal. This paper describes our own implementation of the SIFT and Otsu’s algorithms, analyzes the results of the landmark detection, and highlights future work |
| first_indexed | 2025-11-15T06:34:45Z |
| format | Article |
| id | unimas-12107 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T06:34:45Z |
| publishDate | 2015 |
| publisher | Vaclav Skala - Union Agency |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-121072022-09-15T02:07:45Z http://ir.unimas.my/id/eprint/12107/ Feature extraction and localisation using scale-invariant feature transform on 2.5D image Suk, Ting Pui Jacey-Lynn, Minoi, Terrin, Lim Fradinho Oliveira, João Fyfe Gillies, Duncan T Technology (General) anatomical landmarks, which is a vital initial stage for several applications, such as face recognition, facial analysis and synthesis. Locating facial landmarks in images is an important task in image processing and detecting it automatically still remains challenging. The appearance of facial landmarks may vary tremendously due to facial variations. Detecting and extracting landmarks from raw face data is usually done manually by trained and experienced scientists or clinicians, and the landmarking is a laborious process. Hence, we aim to develop methods to automate as much as possible the process of landmarking facial features. In this paper, we present and discuss our new automatic landmarking method on face data using 2.5-dimensional (2.5D) range images. We applied the Scale-invariant Feature Transform (SIFT) method to extract feature vectors and the Otsu’s method to obtain a general threshold value for landmark localisation. We have also developed an interactive tool to ease the visualisation of the overall landmarking process. The interactive visualization tool has a function which allows users to adjust and explore the threshold values for further analysis, thus enabling one to determine the threshold values for the detection and extraction of important keypoints or/and regions of facial features that are suitable to be used later automatically with new datasets with the same controlled lighting and pose restrictions. We measured the accuracy of the automatic landmarking versus manual landmarking and found the differences to be marginal. This paper describes our own implementation of the SIFT and Otsu’s algorithms, analyzes the results of the landmark detection, and highlights future work Vaclav Skala - Union Agency 2015 Article PeerReviewed text en http://ir.unimas.my/id/eprint/12107/1/No%2041%20%28abstrak%29.pdf Suk, Ting Pui and Jacey-Lynn, Minoi, and Terrin, Lim and Fradinho Oliveira, João and Fyfe Gillies, Duncan (2015) Feature extraction and localisation using scale-invariant feature transform on 2.5D image. 22nd International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, WSCG 2014, Communication Papers Proceedings - in co-operation with EUROGRAPHICS Association. pp. 179-187. ISSN 9.78809E+12 http://www.scopus.com/inward/record.url?eid=2-s2.0-84957922716&partnerID=40&md5=997959304b567010c3b50bb171a2f310 |
| spellingShingle | T Technology (General) Suk, Ting Pui Jacey-Lynn, Minoi, Terrin, Lim Fradinho Oliveira, João Fyfe Gillies, Duncan Feature extraction and localisation using scale-invariant feature transform on 2.5D image |
| title | Feature extraction and localisation using scale-invariant feature transform on 2.5D image |
| title_full | Feature extraction and localisation using scale-invariant feature transform on 2.5D image |
| title_fullStr | Feature extraction and localisation using scale-invariant feature transform on 2.5D image |
| title_full_unstemmed | Feature extraction and localisation using scale-invariant feature transform on 2.5D image |
| title_short | Feature extraction and localisation using scale-invariant feature transform on 2.5D image |
| title_sort | feature extraction and localisation using scale-invariant feature transform on 2.5d image |
| topic | T Technology (General) |
| url | http://ir.unimas.my/id/eprint/12107/ http://ir.unimas.my/id/eprint/12107/ http://ir.unimas.my/id/eprint/12107/1/No%2041%20%28abstrak%29.pdf |