Robust Face Recognition on Adverse 3D Data - Attaining Expression & Occlusion Invariance Using Machine Learning

Abstract

The emerging field of high resolution mobile and inexpensive depth cameras, promise to revolutionize many parts of computer vision. One area in particular where 3D data has been shown to improve performance, is face recognition. Using a combination of local and global pattern matching and a committee of neural networks, this thesis present a robust 3D face recognition approach, decisively outperforming current methods. The system is evaluated on the Bosphorus database, a challenging benchmarking dataset that include face scans with both facial expressions and partial occlusions, captured in angles of up to 90 rotation. The proposed system achieves a recognition rate of 98:9%, which is the highest recognition rate ever reported on the Bosphorus database, improving the state of the art by 5:2%.