Abstract
In this paper, we try to improve face recognition system by taking better advantage of the inherent 3D nature of the face. Face recognition can be greatly improved because the abundant 3D face features can be obtained from different angles. During the simulation, we try to extract the curvatures of the eyes, nose and mouth, which can be used as features for face recognition. The Gaussian curvature is an important component of our work. The distribution of this curvature is used to construct the feature vectors. In order to raise the recognition rate, the projection method is used to intensify the edge information. The mesh modification method is also applied to the 3D mesh models. Finally, the distance between the 3D normalized curvatures of the features is compared between the query and database images for recognition. Even when the facial expression of the query image has changed, we can still achieve a 92% recognition rate with our 3D face recognition algorithms.
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Lin, SW., Hao, SS., Chang, JL., Li, SY. (2013). 3D Face Recognition Based on Curvature Feature Matching, with Expression Variation. In: Lee, S., Cho, H., Yoon, KJ., Lee, J. (eds) Intelligent Autonomous Systems 12. Advances in Intelligent Systems and Computing, vol 193. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33926-4_27
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DOI: https://doi.org/10.1007/978-3-642-33926-4_27
Publisher Name: Springer, Berlin, Heidelberg
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