Abstract
This paper presents a framework for 3D face representation, including pose and depth image normalization. Different than a 2D image, a 3D face itself contains sufficient discriminant information. We propose to map the original 3D coordinates to a depth image using a specific resolution, hence, we can remain the original information in 3D space. 1) Posture correction, we propose 2 simple but effective methods to standardize a face model that is appropriate to handle in following steps; 2) create depth image which remain original measurement information. Tests on a large 3D face dataset containing 2700 3D faces from 450 subjects show that, the proposed normalization provides higher recognition accuracies over other representations.
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Gong, X., Luo, J., Fu, Z. (2013). Normalization for Unconstrained Pose-Invariant 3D Face Recognition. In: Sun, Z., Shan, S., Yang, G., Zhou, J., Wang, Y., Yin, Y. (eds) Biometric Recognition. CCBR 2013. Lecture Notes in Computer Science, vol 8232. Springer, Cham. https://doi.org/10.1007/978-3-319-02961-0_1
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DOI: https://doi.org/10.1007/978-3-319-02961-0_1
Publisher Name: Springer, Cham
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