Face recognition from a representation based on features extracted from range images is explored. Depth and curvature features have several advantages over more traditional intensity-based features. Specifically, curvature descriptors have the potential for higher accuracy in describing surface-based events, are better suited to describe properties of the face in areas such as the cheeks, forehead, and chin, and are viewpoint invariant. Faces are represented in terms of a vector of feature descriptors. Comparisons between two faces is made based on their relationship in the feature space. The author provides a detailed analysis of the accuracy and discrimination of the particular features extracted, and the effectiveness of the recognition system for a test database of 24 faces. Recognition rates are in the range of 80% to 100%. In many cases, feature accuracy is limited more by surface resolution than by the extraction process.<<ETX>>
[1]
Richard O. Duda,et al.
Pattern classification and scene analysis
,
1974,
A Wiley-Interscience publication.
[2]
Takeo Kanade,et al.
Picture Processing System by Computer Complex and Recognition of Human Faces
,
1974
.
[3]
Leo Breiman,et al.
Classification and Regression Trees
,
1984
.
[4]
Peter W. Hallinan.
Recognizing human eyes
,
1991,
Optics & Photonics.
[5]
Gaile G. Gordon,et al.
Face recognition based on depth maps and surface curvature
,
1991,
Optics & Photonics.
[6]
M. Turk,et al.
Eigenfaces for Recognition
,
1991,
Journal of Cognitive Neuroscience.
[7]
G. Gordon.
Face recognition from depth and curvature
,
1992
.