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Face recognition based on depth maps and surface curvature (1991)
| Venue: | SPIE Geometric methods in Computer Vision |
| Citations: | 67 - 0 self |
BibTeX
@INPROCEEDINGS{Gordon91facerecognition,
author = {Gaile G. Gordon},
title = {Face recognition based on depth maps and surface curvature},
booktitle = {SPIE Geometric methods in Computer Vision},
year = {1991},
pages = {234--247}
}
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Abstract
This paper explores the representation of the human face by features based on shape and curvature of the face surface. Curvature captures many features necessary to accurately describe the face, such as the shape of the forehead, jawline, and cheeks, which are not easily detected from standard intensity images. Moreover, the value of curvature at a point on the surface is also viewpointinvariant. Until recently range data of high enough resolution and accuracy to perform useful curvature calculations on the scale of the human face had been unavailable. Although several researchers have worked on the problem of interpreting range data from curved (although usually highly geometrically structured) surfaces, the main approaches have centered on segmentation by signs of mean and Gaussian curvature which have not proved su cient for classi cation of human faces. This paper details the calculation of principal curvature for our particular data set, the calculation of general surface descriptors based on curvature, and the calculation of face speci c descriptors based both on curvature features and aprioriknowledge about the structure of the face. These face speci c descriptors can be incorporated into many di erent recognition strategies. We describe a system which implements one such strategy, depth template comparison, giving excellent recognition rates in our test cases. 1
Keyphrases
depth map surface curvature face recognition human face general surface descriptor standard intensity image speci descriptor principal curvature depth template comparison main approach several researcher paper detail useful curvature calculation su cient excellent recognition rate face speci descriptor face surface range data high enough resolution gaussian curvature many feature classi cation many di erent recognition strategy curvature feature particular data set test case
