TY - GEN
T1 - Fully automatic pose-invariant face recognition via 3D pose normalization
AU - Asthana, Akshay
AU - Marks, Tim K.
AU - Jones, Michael J.
AU - Tieu, Kinh H.
AU - Rohith, M. V.
PY - 2011
Y1 - 2011
N2 - An ideal approach to the problem of pose-invariant face recognition would handle continuous pose variations, would not be database specific, and would achieve high accuracy without any manual intervention. Most of the existing approaches fail to match one or more of these goals. In this paper, we present a fully automatic system for pose-invariant face recognition that not only meets these requirements but also outperforms other comparable methods. We propose a 3D pose normalization method that is completely automatic and leverages the accurate 2D facial feature points found by the system. The current system can handle 3D pose variation up to ±45° in yaw and ±30° in pitch angles. Recognition experiments were conducted on the USF 3D, Multi-PIE, CMU-PIE, FERET, and FacePix databases. Our system not only shows excellent generalization by achieving high accuracy on all 5 databases but also outperforms other methods convincingly.
AB - An ideal approach to the problem of pose-invariant face recognition would handle continuous pose variations, would not be database specific, and would achieve high accuracy without any manual intervention. Most of the existing approaches fail to match one or more of these goals. In this paper, we present a fully automatic system for pose-invariant face recognition that not only meets these requirements but also outperforms other comparable methods. We propose a 3D pose normalization method that is completely automatic and leverages the accurate 2D facial feature points found by the system. The current system can handle 3D pose variation up to ±45° in yaw and ±30° in pitch angles. Recognition experiments were conducted on the USF 3D, Multi-PIE, CMU-PIE, FERET, and FacePix databases. Our system not only shows excellent generalization by achieving high accuracy on all 5 databases but also outperforms other methods convincingly.
UR - http://www.scopus.com/inward/record.url?scp=84856688108&partnerID=8YFLogxK
U2 - 10.1109/ICCV.2011.6126336
DO - 10.1109/ICCV.2011.6126336
M3 - Conference contribution
SN - 9781457711015
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 937
EP - 944
BT - 2011 International Conference on Computer Vision, ICCV 2011
T2 - 2011 IEEE International Conference on Computer Vision, ICCV 2011
Y2 - 6 November 2011 through 13 November 2011
ER -