In this paper, we draw on ideas from the field of statistical shape analysis to construct shape-spaces that span facial expressions and gender, and use the resulting shape-model to perform face recognition under varying expression and gender. Our novel contribution is to show how to construct shape-spaces over fields of surface normals rather than Cartesian landmark points. According to this model face needle-maps (or fields of surface normals) are points in a high-dimensional manifold referred to as a shape-space. We compute geodesic distances to compare the similarity between faces and gender difference. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Ceolin, S., Smith, W. A. P., & Hancock, E. (2008). Facial shape spaces from surface normals. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5112 LNCS, pp. 955–965). https://doi.org/10.1007/978-3-540-69812-8_95
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