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...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Vaclav Skala - Union Agency
2015
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/12107/1/No%2041%20%28abstrak%29.pdf http://ir.unimas.my/id/eprint/12107/ http://www.scopus.com/inward/record.url?eid=2-s2.0-84957922716&partnerID=40&md5=997959304b567010c3b50bb171a2f310 |
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| Summary: | 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 |
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