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Summary: DOMAIN-PARTITIONING RANKBOOST FOR FACE RECOGNITION
Bangpeng YAO1
, Haizhou AI1
, Yoshihisa IJIRI2
, Shihong LAO2
1
Computer Science and Technology Department, Tsinghua University, Beijing 100084, China
2
Sensing and Control Technology Laboratory, Omron Corporation, Kyoto 619-0283, Japan
Email: ahz@mail.tsinghua.edu.cn
ABSTRACT
In this paper we propose a domain partitioning RankBoost
approach for face recognition. This method uses Local Ga-
bor Binary Pattern Histogram (LGBPH) features for face
representation, and adopts RankBoost to select the most
discriminative features. Unlike the original RankBoost algo-
rithm in [1], weak hypotheses in our method make their pre-
dictions based on a partitioning of the similarity domain.
Since the domain partitioning approach handles the loss
function of a ranking problem directly, it can achieve a
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