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on extracting the topology statistics within the local surface sampled at ... are key-points that provide identification of surface anatomy forms between and.

Robust Feature Extraction for Range Images Interpretation using Local Topology Statistics Tsz-Wai Rachel Lo1 , J. Paul Siebert1 , and Ashraf F. Ayoub2 1

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Department of Computing Science, University of Glasgow, Glasgow, G12 8QQ, UK lotr|[email protected] Glasgow Dental Hospital and School, 378 Sauchiehall Street, Glasgow, G2 3JZ, UK [email protected]

Abstract This paper presents a robust methodology for extracting feature vectors, which exhibit invariance to viewpoint rotations, from 2.5dimensional (2.5D) range images of human faces. Our approach is based on extracting the topology statistics within the local surface sampled at an anatomic landmark location. These statistics are used to define a feature vector that encapsulates the shape of the sampled surface by means of the relative frequencies of classified surface types and their orientations. The invariance of this feature vector against rotational changes in viewpoint for all three Euler axes has been validated using 50 frontal range images of human faces, each labelled with 28 anatomic landmarks. Our results indicate that the invariance of the feature vectors to inplane and out-of-plane rotations of