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- W1550113892 abstract "Gender recognition is one of fundamental tasks of face image analysis. Most of the existing studies have focused on face images acquired under controlled conditions. However, real-world applications require gender classification on real-life faces, which is much more challenging due to significant appearance variations in unconstrained scenarios. In this paper, we investigate gender recognition on real-life faces using the recently built database, the Labeled Faces in the Wild (LFW). Local Binary Patterns (LBP) is employed to describe faces, and Adaboost is used to select the discriminative LBP features. We obtain the performance of 94.44% by applying Support Vector Machine (SVM) with the boosted LBP features. The public database used in this study makes future benchmark and evaluation possible." @default.
- W1550113892 created "2016-06-24" @default.
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- W1550113892 date "2010-01-01" @default.
- W1550113892 modified "2023-09-25" @default.
- W1550113892 title "Gender Classification on Real-Life Faces" @default.
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- W1550113892 doi "https://doi.org/10.1007/978-3-642-17691-3_30" @default.
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