A comprehensive overview of feature representation for biometric recognition

被引：51
作者：
Rida, Imad [1 ]
Al-Maadeed, Noor [1 ]
Al-Maadeed, Somaya [1 ]
Bakshi, Sambit [2 ]
机构：
[1] Qatar Univ, Dept Comp Sci & Engn, Doha, Qatar
[2] Natl Inst Technol Rourkela, Dept Comp Sci & Engn, Rourkela 769008, India
来源：
MULTIMEDIA TOOLS AND APPLICATIONS | 2020年 / 79卷 / 7-8期
关键词：
Biometrics; Feature representation; Dimensionality reduction; Feature selection; Decomposition learning; DIMENSIONALITY REDUCTION; FEATURE-SELECTION; PRESERVING PROJECTIONS; SPARSE REPRESENTATION; MUTUAL INFORMATION; GROUP LASSO; CLASSIFICATION; REGRESSION; ALGORITHM; MODEL;
D O I：
10.1007/s11042-018-6808-5
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The performance of any biometric recognition system heavily dependents on finding a good and suitable feature representation space where observations from different classes are well separated. Unfortunately, finding this proper representation is a challenging problem which has taken a huge interest in machine learning and computer vision communities. In the this paper we present a comprehensive overview of the different existing feature representation techniques. This is carried out by introducing simple and clear taxonomies as well as effective explanation of the prominent techniques. This is intended to guide the neophyte and provide researchers with state-of-the-art approaches in order to help advance the research topic in biometrics.
引用
收藏
页码：4867 / 4890
页数：24
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