International Journal of Biometrics http://www.inderscience.com/browse/index.php?journalID=285&year=2012&vol=4&issue=1 Inderscience Publishers Ltd en-uk International Journal of Biometrics 1755-8301 1755-831X © 2012 Inderscience Publishers Ltd editor@inderscience.com https://www.inderscience.com/images/files/coverImgs/ijbm_scoverijbm.jpg http://www.inderscience.com/browse/index.php?journalID=285&year=2012&vol=4&issue=1 http://www.inderscience.com/link.php?id=44289 Biometric identification is becoming a leading technology for identity management and security systems. Nonetheless, the use of counterfeit elastic fingerprints ('spoofing') may break these measures. In this paper, we address the problem of fingerprint spoofing based solely on image features extracted from 2D fingerprint images. By combining several low-accuracy methods, a robust high-performance classifier for real vs. fake fingerprint images is constructed. Its high accuracy is demonstrated on a large fingerprint database. The method thus shows high potential for improving existing fingerprint authentication devices. Classification of fingerprint images to real vs. spoof
Tatiana Barsky; Ariel Tankus; Yehezkel Yeshurun
International Journal of Biometrics, Vol. 4, No. 1 (2012) pp. 1 - 21
Biometric identification is becoming a leading technology for identity management and security systems. Nonetheless, the use of counterfeit elastic fingerprints ('spoofing') may break these measures. In this paper, we address the problem of fingerprint spoofing based solely on image features extracted from 2D fingerprint images. By combining several low-accuracy methods, a robust high-performance classifier for real vs. fake fingerprint images is constructed. Its high accuracy is demonstrated on a large fingerprint database. The method thus shows high potential for improving existing fingerprint authentication devices.

]]> 10.1504/IJBM.2012.044289 International Journal of Biometrics, Vol. 4, No. 1 (2012) pp. 1 - 21 Tatiana Barsky; Ariel Tankus; Yehezkel Yeshurun School of Computer Science, Tel-Aviv University, Tel-Aviv 69978, Israel. ' Department of Biomedical Engineering Technion Israel Institute of Technology, Haifa 32000, Israel. ' School of Computer Science, Tel-Aviv University, Tel-Aviv 69978, Israel ?ngerprint images anti-spoo?ng anti-faking ACL anti-counterfeit layer fingerprint authentication biometric identi?cation identity theft feature extraction image classification fingerprint classi?cation combination of algorithms biometrics fingerprint spoofing. 2011-12-19T23:20:50-05:00 4 1 1 21 2011-12-19T23:20:50-05:00 http://www.inderscience.com/link.php?id=44290 The paper discusses several issues of visual similarity in face detection and recognition. Using a straightforward concept of keypoint correspondences, a method is proposed to formalise the subjective impressions of 'similar faces', 'similar eyes', 'similar chins', etc. The method exploits the mechanism of affine near-duplicate fragment detection originally proposed for visual information retrieval. It is shown that using such a method, a simple and relatively reliable face detection/identification systems can be build without any model (or training) of human faces, which can work with images containing multiple faces shown on random backgrounds. Additionally, it is proposed how the same approach can be used to optimise databases of face images and to identify individuals who are at higher risks of mistaken face identification. Visual similarity issues in face recognition
Andrzej Sluzek; Mariusz Paradowski
International Journal of Biometrics, Vol. 4, No. 1 (2012) pp. 22 - 37
The paper discusses several issues of visual similarity in face detection and recognition. Using a straightforward concept of keypoint correspondences, a method is proposed to formalise the subjective impressions of 'similar faces', 'similar eyes', 'similar chins', etc. The method exploits the mechanism of affine near-duplicate fragment detection originally proposed for visual information retrieval. It is shown that using such a method, a simple and relatively reliable face detection/identification systems can be build without any model (or training) of human faces, which can work with images containing multiple faces shown on random backgrounds. Additionally, it is proposed how the same approach can be used to optimise databases of face images and to identify individuals who are at higher risks of mistaken face identification.

]]> 10.1504/IJBM.2012.044290 International Journal of Biometrics, Vol. 4, No. 1 (2012) pp. 22 - 37 Andrzej Sluzek; Mariusz Paradowski Khalifa University, Abu Dhabi Campus, P.O. Box 127788, Abu Dhabi, UAE. ' Wroclaw University of Technology, Institute of Informatics, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland visual similarity face recognition keypoints image matching affine transformations face detection near-duplicate fragment detection biometrics mistaken identification. 2011-12-19T23:20:50-05:00 4 1 22 37 2011-12-19T23:20:50-05:00 http://www.inderscience.com/link.php?id=44291 This paper presents a novel dynamic threshold approach to discriminate skin pixels and non-skin pixels in colour images. Fixed decision boundaries (or fixed threshold) classification approaches are successfully applied to detect human skin tone in colour images. These fixed thresholds mostly failed in two situations as they only search for a certain skin colour range: any non-skin object may be classified as skin if non-skin objects's colour values belong to fixed threshold range; any true skin may be mistakenly classified as non-skin if the skin colour values do not belong to fixed threshold range. Therefore in this paper, instead of predefined fixed thresholds, novel online learned dynamic thresholds are used to overcome the above drawbacks. The experimental results show that our method is robust in overcoming these drawbacks. A dynamic threshold approach for skin tone detection in colour images
Pratheepan Yogarajah; Joan Condell; Kevin Curran; Paul McKevitt; Abbas Cheddad
International Journal of Biometrics, Vol. 4, No. 1 (2012) pp. 38 - 55
This paper presents a novel dynamic threshold approach to discriminate skin pixels and non-skin pixels in colour images. Fixed decision boundaries (or fixed threshold) classification approaches are successfully applied to detect human skin tone in colour images. These fixed thresholds mostly failed in two situations as they only search for a certain skin colour range: any non-skin object may be classified as skin if non-skin objects's colour values belong to fixed threshold range; any true skin may be mistakenly classified as non-skin if the skin colour values do not belong to fixed threshold range. Therefore in this paper, instead of predefined fixed thresholds, novel online learned dynamic thresholds are used to overcome the above drawbacks. The experimental results show that our method is robust in overcoming these drawbacks.

]]> 10.1504/IJBM.2012.044291 International Journal of Biometrics, Vol. 4, No. 1 (2012) pp. 38 - 55 Pratheepan Yogarajah; Joan Condell; Kevin Curran; Paul McKevitt; Abbas Cheddad School of Computing and Intelligent Systems (SCIS), University of Ulster, Northern Ireland, BT48 7JL, UK. ' School of Computing and Intelligent Systems (SCIS), University of Ulster, Northern Ireland, BT48 7JL, UK. ' School of Computing and Intelligent Systems (SCIS), University of Ulster, Northern Ireland, BT48 7JL, UK. ' School of Computing and Intelligent Systems, Magee campus, University of Ulster, UK. ' Umeå Centre for Molecular Medicine (UCMM), Umeå Universitet, 901 87 Umeå, Sweden. skin tone detection dynamic threshold colour images biometrics. 2011-12-19T23:20:50-05:00 4 1 38 55 2011-12-19T23:20:50-05:00 http://www.inderscience.com/link.php?id=44295 A new iris recognition method based on a robust iris segmentation approach is presented in this paper for improving iris recognition performance. The robust iris segmentation approach applies power-law transformations for more accurate detection of the pupil region, which significantly reduces the candidate limbic boundary search space for increasing detection accuracy and efficiency. The limbic circle having a centre within close range of the pupil centre is selectively detected, and the eyelid detection approach thus leads to improved iris recognition performance. Experiments using the Iris Challenge Evaluation (ICE) database show the effectiveness of the proposed method. Iris recognition based on robust iris segmentation and image enhancement
Abhishek Verma; Chengjun Liu; Jiancheng (Kevin) Jia
International Journal of Biometrics, Vol. 4, No. 1 (2012) pp. 56 - 76
A new iris recognition method based on a robust iris segmentation approach is presented in this paper for improving iris recognition performance. The robust iris segmentation approach applies power-law transformations for more accurate detection of the pupil region, which significantly reduces the candidate limbic boundary search space for increasing detection accuracy and efficiency. The limbic circle having a centre within close range of the pupil centre is selectively detected, and the eyelid detection approach thus leads to improved iris recognition performance. Experiments using the Iris Challenge Evaluation (ICE) database show the effectiveness of the proposed method.

]]> 10.1504/IJBM.2012.044295 International Journal of Biometrics, Vol. 4, No. 1 (2012) pp. 56 - 76 Abhishek Verma; Chengjun Liu; Jiancheng (Kevin) Jia Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA. ' Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA. ' Department of Test Engineering, International Game Technology, Reno, NV 89521, USA iris recognition iris segmentation power-law transformations ICE iris challenge evaluation biometrics eyelid detection. 2011-12-19T23:20:50-05:00 4 1 56 76 2011-12-19T23:20:50-05:00 http://www.inderscience.com/link.php?id=44296 Face retrieval has received much attention in recent years. This paper comparatively studied five feature description methods for face representation, including Local Binary Pattern (LBP), Gabor feature, Gray Level Co-occurrence Matrices (GLCM), Pyramid Histogram of Oriented Gradient (PHOG) and Curvelet Transform (CT). The problem of large dimensionalities of the extracted features was addressed by employing a manifold learning method called Spectral Regression (SR). A fusion scheme was proposed by aggregating the distance metrics. Experiments illustrated that dimension reduced features are more efficient and the fusion scheme can offer much enhanced performance. A 98% rank 1 accuracy was obtained for the AR faces and 92% for the FERET faces. Spectral Regression dimension reduction for multiple features facial image retrieval
Bailing Zhang; Yongsheng Gao
International Journal of Biometrics, Vol. 4, No. 1 (2012) pp. 77 - 101
Face retrieval has received much attention in recent years. This paper comparatively studied five feature description methods for face representation, including Local Binary Pattern (LBP), Gabor feature, Gray Level Co-occurrence Matrices (GLCM), Pyramid Histogram of Oriented Gradient (PHOG) and Curvelet Transform (CT). The problem of large dimensionalities of the extracted features was addressed by employing a manifold learning method called Spectral Regression (SR). A fusion scheme was proposed by aggregating the distance metrics. Experiments illustrated that dimension reduced features are more efficient and the fusion scheme can offer much enhanced performance. A 98% rank 1 accuracy was obtained for the AR faces and 92% for the FERET faces.

]]> 10.1504/IJBM.2012.044296 International Journal of Biometrics, Vol. 4, No. 1 (2012) pp. 77 - 101 Bailing Zhang; Yongsheng Gao Department of Computer Science and Software Engineering, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China. ' School of Engineering, Griffith University, QLD 4111, Australia face images face retrieval image retrieval dimension reduction multiple feature fusion LBP local binary pattern Gabor feature curvelet transform PHOG pyramid histogram of oriented gradient feature extraction biometrics. 2011-12-19T23:20:50-05:00 4 1 77 101 2011-12-19T23:20:50-05:00