TY - GEN
T1 - Ensemble feature selection in face recognition
T2 - 11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012
AU - Alelyani, Salem
AU - Liu, Huan
PY - 2012/12/1
Y1 - 2012/12/1
N2 - Ensemble feature selection is known for its robustness and generalization of highly accurate predictive models. In this paper, we use different filter-based feature selection methods in an ensemble manner to improve face recognition. The goal is to distinguish human faces from avatar faces. Our approach was able to achieve very high accuracy, 99%, using less than 1% of the pixels in each image. This was obtained after removing irrelevant features which is known to degrade learning performance and model stability.
AB - Ensemble feature selection is known for its robustness and generalization of highly accurate predictive models. In this paper, we use different filter-based feature selection methods in an ensemble manner to improve face recognition. The goal is to distinguish human faces from avatar faces. Our approach was able to achieve very high accuracy, 99%, using less than 1% of the pixels in each image. This was obtained after removing irrelevant features which is known to degrade learning performance and model stability.
UR - http://www.scopus.com/inward/record.url?scp=84873575280&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84873575280&partnerID=8YFLogxK
U2 - 10.1109/ICMLA.2012.182
DO - 10.1109/ICMLA.2012.182
M3 - Conference contribution
AN - SCOPUS:84873575280
SN - 9780769549132
T3 - Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012
SP - 588
EP - 591
BT - Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012
Y2 - 12 December 2012 through 15 December 2012
ER -