A comprehensive overview of biometric fusion

被引:140
作者
Singh, Maneet [1 ]
Singh, Richa [1 ]
Ross, Arun [2 ]
机构
[1] IIIT Delhi, New Delhi, India
[2] Michigan State Univ, E Lansing, MI 48824 USA
基金
美国国家科学基金会;
关键词
Biometrics; Information fusion; Multibiometrics; Soft biometrics; Continuous authentication; Privacy; Security; Cryptosystems; Spoof detection; Social networks; SCORE-LEVEL FUSION; FINGERPRINT LIVENESS DETECTION; PRESENTATION ATTACK DETECTION; SOFT BIOMETRICS; FACE RECOGNITION; PERFORMANCE PREDICTION; IDENTITY PREDICTION; GAIT RECOGNITION; SOCIAL-CONTEXT; QUALITY;
D O I
10.1016/j.inffus.2018.12.003
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The performance of a biometric system that relies on a single biometric modality (e.g., fingerprints only) is often stymied by various factors such as poor data quality or limited scalability. Multibiometric systems utilize the principle of fusion to combine information from multiple sources in order to improve recognition accuracy whilst addressing some of the limitations of single-biometric systems. The past two decades have witnessed the development of a large number of biometric fusion schemes. This paper presents an overview of biometric fusion with specific focus on three questions: what to fuse, when to fuse, and how to fuse. A comprehensive review of techniques incorporating ancillary information in the biometric recognition pipeline is also presented. In this regard, the following topics are discussed: (i) incorporating data quality in the biometric recognition pipeline; (ii) combining soft biometric attributes with primary biometric identifiers; (iii) utilizing contextual information to improve biometric recognition accuracy; and (iv) performing continuous authentication using ancillary information. In addition, the use of information fusion principles for presentation attack detection and multibiometric cryptosystems is also discussed. Finally, some of the research challenges in biometric fusion are enumerated. The purpose of this article is to provide readers a comprehensive overview of the role of information fusion in biometrics.
引用
收藏
页码:187 / 205
页数:19
相关论文
共 212 条
[1]  
Abaza A., 2009, IEEE INT C BIOM THEO
[2]  
Abreu M, 2009, LECT NOTES COMPUT SC, V5707, P348, DOI 10.1007/978-3-642-04391-8_45
[3]   Enhancing Identity Prediction Using a Novel Approach to Combining Hard- and Soft-Biometric Information [J].
Abreu, Marjory Cristiany Da Costa ;
Fairhurst, Michael .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS, 2011, 41 (05) :599-607
[4]   Face Presentation Attack with Latex Masks in Multispectral Videos [J].
Agarwal, Akshay ;
Yadav, Daksha ;
Kohli, Naman ;
Singh, Richa ;
Vatsa, Mayank ;
Noore, Afzel .
2017 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW), 2017, :275-283
[5]   Soft biometrics-combining body weight and fat measurements with fingerprint biometrics [J].
Ailisto, H ;
Vildjounaite, E ;
Lindholm, M ;
Mäkelä, SM ;
Peltola, J .
PATTERN RECOGNITION LETTERS, 2006, 27 (05) :325-334
[6]   Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey [J].
Akhtar, Naveed ;
Mian, Ajmal .
IEEE ACCESS, 2018, 6 :14410-14430
[7]  
ALAHMAD Y, 2018, IEEE IPCCC
[8]  
Altinok A., 2003, WORKSH MULT US AUTH
[9]  
Anguelov D, 2007, PROC CVPR IEEE, P673
[10]  
[Anonymous], P SPIE C BIOMETRIC T