TY - GEN
T1 - SODA-boosting and its application to gender recognition
AU - Xu, N.
AU - Huang, Thomas S.
PY - 2007
Y1 - 2007
N2 - In this paper we propose a novel boosting based classification algorithm, SODA-Boosting (where SODA stands for Second Order Discriminant Analysis). Unlike the conventional AdaBoost based algorithms widely applied in computer vision, SODA-Boosting does not involve time consuming procedures to search a huge feature pool in every iteration during the training stage. Instead, in each iteration SODA-Boosting efficiently computes discriminative weak classifiers in closed-form, based on reasonable hypotheses on the distribution of the weighted training samples. As an application, SODA-Boosting is employed for image based gender recognition. Experimental results on publicly available FERET database are reported. The proposed algorithm achieved accuracy comparable to state-of-the-art approaches, and demonstrated superior performance to relevant boosting based algorithms.
AB - In this paper we propose a novel boosting based classification algorithm, SODA-Boosting (where SODA stands for Second Order Discriminant Analysis). Unlike the conventional AdaBoost based algorithms widely applied in computer vision, SODA-Boosting does not involve time consuming procedures to search a huge feature pool in every iteration during the training stage. Instead, in each iteration SODA-Boosting efficiently computes discriminative weak classifiers in closed-form, based on reasonable hypotheses on the distribution of the weighted training samples. As an application, SODA-Boosting is employed for image based gender recognition. Experimental results on publicly available FERET database are reported. The proposed algorithm achieved accuracy comparable to state-of-the-art approaches, and demonstrated superior performance to relevant boosting based algorithms.
UR - http://www.scopus.com/inward/record.url?scp=38149079735&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=38149079735&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-75690-3_15
DO - 10.1007/978-3-540-75690-3_15
M3 - Conference contribution
AN - SCOPUS:38149079735
SN - 9783540756897
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 193
EP - 204
BT - Analysis and Modeling of Faces and Gestures - Third International Workshop, AMFG 2007, Proceedings
PB - Springer
T2 - 3rd International Workshop on Analysis and Modeling of Faces and Gestures, AMFG 2007
Y2 - 20 October 2007 through 20 October 2007
ER -