Combining perspiration- and morphology-based static features for fingerprint liveness detection

Marasco, Emanuela; Sansone, Carlo

doi:10.1016/j.patrec.2012.01.009

Cited by 94 publications

(46 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [53], a novel fingerprint liveness detection method combining perspiration and morphological features was presented and evaluated on the LivDet09 database following the same protocol (training and test sets) used in the competition. In that work, comparative results were reported with particular implementations (from the authors) of the techniques proposed in: [54], based on the wavelet analysis of the finger tip texture; [55], based on the curvelet analysis of the finger tip texture; and [56] based on the combination of local ridge frequencies and multiresolution texture analysis.…”

Section: ) Results: Fingerprints-spoofing Livdetmentioning

confidence: 99%

Image Quality Assessment for Fake Biometric Detection: Application to Iris, Fingerprint, and Face Recognition

Galbally

Marcel

Fiérrez

2014

IEEE Trans. on Image Process.

532

302

View full text Add to dashboard Cite

Esta es la versión de autor del artículo publicado en: This is an author produced version of a paper published in: Abstract-To ensure the actual presence of a real legitimate trait in contrast to a fake self-manufactured synthetic or reconstructed sample, is a significant problem in biometric authentication, which requires the development of new and efficient protection measures. In this work we present a novel software-based fake detection method which can be used in multiple biometric systems to detect different types of fraudulent access attempts. The objective of the proposed system is to enhance the security of biometric recognition frameworks, by adding liveness assessment in a fast, userfriendly and non-intrusive manner, through the use of Image Quality Assessment (IQA). The proposed approach presents a very low degree of complexity which makes it suitable for real-time applications, using 25 general image quality features extracted from one image (i.e., the same acquired for authentication purposes) to distinguish between legitimate and impostor samples. The experimental results, obtained on publicly available datasets of fingerprint, iris and 2-D face, show that the proposed method is highly competitive compared to other state-of-the-art approaches and that the analysis of the general image quality of real biometric samples reveals highly valuable information that may be very efficiently used to discriminate them from fake traits.

show abstract

Section: ) Results: Fingerprints-spoofing Livdetmentioning

confidence: 99%

Image Quality Assessment for Fake Biometric Detection: Application to Iris, Fingerprint, and Face Recognition

Galbally

Marcel

Fiérrez

2014

IEEE Trans. on Image Process.

532

302

View full text Add to dashboard Cite

show abstract

“…The scores, therefore, correspond to four different matching scenarios: Live vs Live, Live vs Spoof, Spoof vs Live, and Spoof vs Spoof. For each image, the liveness measure was extracted by using an algorithm which combines morphological and perspiration-based characteristics [11], [12].…”

Section: Datasetmentioning

confidence: 99%

Combining match scores with liveness values in a fingerprint verification system

Marasco

Ding

Ross

2012

2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)

Self Cite

View full text Add to dashboard Cite

“…However, it is possible to deceive fingerprint recognition systems by presenting a wellduplicated artificial fingerprint [4]. Artificial fingerprints, which carry the identity of enrolled users and created to attempt to gain unauthorized access, are referred as spoof [4].…”

Section: Introductionmentioning

confidence: 99%

“…Artificial fingerprints, which carry the identity of enrolled users and created to attempt to gain unauthorized access, are referred as spoof [4]. The attackers can make a spoof fingerprint to achieve unauthorized access.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fingerprint Liveness Detection Using Gray Level Co-Occurrence Matrix Based Texture Feature

Yuan¹,

Xia²,

Sun³

et al. 2016

IJGDC

View full text Add to dashboard Cite

show abstract

Combining perspiration- and morphology-based static features for fingerprint liveness detection

Cited by 94 publications

References 11 publications

Image Quality Assessment for Fake Biometric Detection: Application to Iris, Fingerprint, and Face Recognition

Image Quality Assessment for Fake Biometric Detection: Application to Iris, Fingerprint, and Face Recognition

Combining match scores with liveness values in a fingerprint verification system

Fingerprint Liveness Detection Using Gray Level Co-Occurrence Matrix Based Texture Feature

Contact Info

Product

Resources

About