TY - GEN
T1 - A novel multi-stage classifier for face recognition
AU - Kuo, Chen Hui
AU - Lee, Jiann Der
AU - Chan, Tung Jung
PY - 2007
Y1 - 2007
N2 - A novel face recognition scheme based on multi-stages classifier, which includes methods of support vector machine (SVM), Eigenface, and random sample consensus (RANSAC), is proposed in this paper. The whole decision process is conducted cascade coarse-to-fine stages. The first stage adopts one-against-one-SVM (OAO-SVM) method to choose two possible classes best similar to the testing image. In the second stage, "Eigenface" method was employed to select one prototype image with the minimum distance to the testing image in each of the two classes chosen. Finally, the real class is determined by comparing the geometric similarity, as done by "RANSAC" method, between these prototype images and the testing images. This multi-stage face recognition system has been tested on Olivetti Research Laboratory (ORL) face databases, and its experimental results give evidence that the proposed approach outperforms the other approaches either based on the single classifier or multi-parallel classifier, it can even obtain a nearly 100 percent recognition accuracy.
AB - A novel face recognition scheme based on multi-stages classifier, which includes methods of support vector machine (SVM), Eigenface, and random sample consensus (RANSAC), is proposed in this paper. The whole decision process is conducted cascade coarse-to-fine stages. The first stage adopts one-against-one-SVM (OAO-SVM) method to choose two possible classes best similar to the testing image. In the second stage, "Eigenface" method was employed to select one prototype image with the minimum distance to the testing image in each of the two classes chosen. Finally, the real class is determined by comparing the geometric similarity, as done by "RANSAC" method, between these prototype images and the testing images. This multi-stage face recognition system has been tested on Olivetti Research Laboratory (ORL) face databases, and its experimental results give evidence that the proposed approach outperforms the other approaches either based on the single classifier or multi-parallel classifier, it can even obtain a nearly 100 percent recognition accuracy.
KW - Computational geometry
KW - Computational methods
KW - Database systems
KW - Decision theory
KW - Eigenvalues and eigenfunctions
KW - Multi-stage classifiers
KW - Olivetti Research Laboratory (ORL)
KW - Prototype image
KW - Support vector machines
UR - http://www.scopus.com/inward/record.url?scp=38149073354&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-76390-1_62
DO - 10.1007/978-3-540-76390-1_62
M3 - 会议稿件
AN - SCOPUS:38149073354
SN - 9783540763895
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 631
EP - 640
BT - Computer Vision - ACCV 2007 - 8th Asian Conference on Computer Vision, Proceedings
PB - Springer Verlag
T2 - 8th Asian Conference on Computer Vision, ACCV 2007
Y2 - 18 November 2007 through 22 November 2007
ER -