Evaluation of Keypoint Descriptors for Gender Recognition

被引：0

作者：

Soledad Iglesias, Florencia ^{[1
]}

Elena Buemi, Maria ^{[1
]}

Acevedo, Daniel ^{[1
]}

Jacobo-Berlles, Julio ^{[1
]}

机构：

[1] Univ Buenos Aires, Fac Ciencias Exactas & Nat, Dept Comp, RA-1053 Buenos Aires, DF, Argentina

来源：

PROGRESS IN PATTERN RECOGNITION IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS, CIARP 2014 | 2014年 / 8827卷

关键词：

Gender recognition; LBP; Keypoint Descriptors;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Gender recognition is a relevant problem due to the number and importance of its possible application areas. The challenge is to achieve high recognition rates in the shortest possible time. Most studies are based on Local Binary Patterns (LBP) and its variants to estimate gender. In this paper, we propose the use of Binary Robust Independent Elementary Features (BRIEF), Oriented FAST and Rotated BRIEF (ORB) and Binary Robust Invariant Scalable Keypoints (BRISK) in gender recognition due to their good performance and speed. The aim is to show that ORB and BRISK are faster than LBP but allow to achieve similar recognition rates, which makes them suitable for real-time systems. For the best of our knowledge, it has not been studied in literature.

引用

页码：564 / 571

页数：8

共 20 条

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