A comprehensive overview of feature representation for biometric recognition

被引：0
作者：
Imad Rida
Noor Al-Maadeed
Somaya Al-Maadeed
Sambit Bakshi
机构：
[1] Qatar University,Department of Computer Science and Engineering
[2] National Institute of Technology Rourkela,Department of Computer Science and Engineering
来源：
Multimedia Tools and Applications | 2020年 / 79卷
关键词：
Biometrics; Feature representation; Dimensionality reduction; Feature selection; Decomposition learning;
D O I：
暂无
中图分类号：
学科分类号：
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.
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页码：4867 / 4890
页数：23
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共 213 条
  • [1] Maadeed Somaya Al(2018)Palmprint identification using sparse and dense hybrid representation Multimedia Tools and Applications 78 5665-5679
  • [2] Jiang Xudong(1999)Natural gradient learning for over-and under-complete bases in ICA Neural Comput 11 1875-1883
  • [3] Rida Imad(2007)Feature selection and classification of hyperspectral images with support vector machines IEEE Geosci Remote Sens Lett 4 674-677
  • [4] Bouridane Ahmed(2008)Consistency of the group lasso and multiple kernel learning J Mach Learn Res 9 1179-1225
  • [5] Amari S(1994)Using mutual information for selecting features in supervised neural net learning IEEE Trans Neural Netw 5 537-550
  • [6] Archibald R(2013)Representation learning: a review and new perspectives IEEE Trans Pattern Anal Mach Intell 35 1798-1828
  • [7] Fann G(2002)Stability and generalization J Mach Learn Res 2 499-526
  • [8] Bach F(2014)A survey on feature selection methods Comput Electr Eng 40 16-28
  • [9] Battiti R(2016)Convex sparse PCA for unsupervised feature learning ACM Trans Knowl Discov Data (TKDD) 11 3-6373
  • [10] Bengio Y(2018)Local Adaptive Projection Framework for Feature Selection of Labeled and Unlabeled Data IEEE Transactions on Neural Networks and Learning Systems 29 6362-866
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