In this paper, we present a novel algorithm for 3D face recognition that is robust to the rotations and translations of the face models. Based on the Iterative Closest Point algorithm, a template based registration strategy is proposed for data normalization. Back-Propagation neural networks are then constructed to perform recognition tasks. The proposed algorithm is general purpose and can be applied for common 3D object recognitions. Experimental results illustrate that the algorithm is effective and robust.
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||1st International Conference on Pattern Recognition and Machine Intelligence, PReMI 2005|
|Period||20/12/05 → 22/12/05|
- Computer Science(all)
- Biochemistry, Genetics and Molecular Biology(all)
- Theoretical Computer Science