Most recent issue published online in the International Journal of Biometrics.
International Journal of Biometrics
http://www.inderscience.com/browse/index.php?journalID=285&year=2024&vol=16&issue=2
Inderscience Publishers Ltd
en-uk
support@inderscience.com
International Journal of Biometrics
1755-8301
1755-831X
© 2024 Inderscience Enterprises Ltd.
© 2024 Inderscience Publishers Ltd
editor@inderscience.com
International Journal of Biometrics
https://www.inderscience.com/images/files/coverImgs/ijbm_scoverijbm.jpg
http://www.inderscience.com/browse/index.php?journalID=285&year=2024&vol=16&issue=2
-
Fingerprint multiple-class classifier: performance evaluation on known and unknown fingerprint spoofing materials
http://www.inderscience.com/link.php?id=137088
Fingerprint recognition is a popular and reliable biometric technology used in many security-sensitive applications. However, the use of fake fingerprints made from ubiquitous spoofing materials poses a significant threat to security systems. While several studies have proposed binary classifiers to detect fingerprint presentation attacks, relatively few have explored the effectiveness of multiple-class classifiers in detecting known and unknown spoofs. In this study, we evaluated the efficacy of multiple-class classifiers using deep transfer learning to detect presentation attacks made with different spoofing materials. Our experiments on the LivDet 2009-2015 datasets showed that while a classifier model developed without data augmentation performed better on known spoofs, it showed poor performance on cross-material detection of all seven fingerprint spoofing materials. These results suggest that modelling a multiple-class classifier is not an efficient approach for detecting cross-material presentation attacks in fingerprint recognition systems.
Fingerprint multiple-class classifier: performance evaluation on known and unknown fingerprint spoofing materials
Divine Senanu Ametefe; Suzi Seroja Sarnin; Darmawaty Mohd Ali; Dah B. John; Abdulmalik Adozuka Aliu
International Journal of Biometrics, Vol. 16, No. 2 (2024) pp. 113 - 132
Fingerprint recognition is a popular and reliable biometric technology used in many security-sensitive applications. However, the use of fake fingerprints made from ubiquitous spoofing materials poses a significant threat to security systems. While several studies have proposed binary classifiers to detect fingerprint presentation attacks, relatively few have explored the effectiveness of multiple-class classifiers in detecting known and unknown spoofs. In this study, we evaluated the efficacy of multiple-class classifiers using deep transfer learning to detect presentation attacks made with different spoofing materials. Our experiments on the LivDet 2009-2015 datasets showed that while a classifier model developed without data augmentation performed better on known spoofs, it showed poor performance on cross-material detection of all seven fingerprint spoofing materials. These results suggest that modelling a multiple-class classifier is not an efficient approach for detecting cross-material presentation attacks in fingerprint recognition systems.]]>
10.1504/IJBM.2024.137088
International Journal of Biometrics, Vol. 16, No. 2 (2024) pp. 113 - 132
Divine Senanu Ametefe
Suzi Seroja Sarnin
Darmawaty Mohd Ali
Dah B. John
Abdulmalik Adozuka Aliu
Wireless Communication Technology Group (WiCOT), College of Engineering, School of Electrical Engineering, Universiti Teknologi MARA (UiTM), 40450 Shah Alam, Selangor, Malaysia ' Wireless Communication Technology Group (WiCOT), College of Engineering, School of Electrical Engineering, Universiti Teknologi MARA (UiTM), 40450 Shah Alam, Selangor, Malaysia ' Wireless Communication Technology Group (WiCOT), College of Engineering, School of Electrical Engineering, Universiti Teknologi MARA (UiTM), 40450 Shah Alam, Selangor, Malaysia ' School of Information Science, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA (UiTM), 40150 Puncak Perdana, Selangor, Malaysia ' College of Built Environment, Universiti Teknologi MARA (UiTM), 40450 Shah Alam, Selangor, Malaysia
fingerprint spoofing
multiple-class classifier
known spoofing materials
unknown spoofing materials
deep transfer learning
2024-03-01T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
16
2
113
132
2024-03-01T23:20:50-05:00
-
A unique approach towards keystroke dynamics-based entry-point user access control
http://www.inderscience.com/link.php?id=137082
Access control is an essential security service for computing devices, applications, and information. Among the different entry-point user access controls, keystroke dynamics (KDs) has gained popularity owing to its several merits, such as low cost, ease of usage, etc. In this study, we proposed a unique distance-based anomaly detector together with an appropriate template construction method leading to more realistic and accurate results. We validated our approach with ten standard datasets and compared the performance with 50 state-of-the-art anomaly detectors. In our consideration, recent anomaly detectors have been re-evaluated in the same experimental setting for sound comparison. An analysis of variance (ANOVA) was conducted to compare the performance of our approach to those detectors in both desktops and recent smartphones. This study provides an in-depth understanding of each detector's performance which will aid in the design of efficient KD-based access control in the next generation of smart devices and applications.
A unique approach towards keystroke dynamics-based entry-point user access control
Soumen Roy; Devadatta Sinha; Rajat Kumar Pal; Utpal Roy
International Journal of Biometrics, Vol. 16, No. 2 (2024) pp. 133 - 157
Access control is an essential security service for computing devices, applications, and information. Among the different entry-point user access controls, keystroke dynamics (KDs) has gained popularity owing to its several merits, such as low cost, ease of usage, etc. In this study, we proposed a unique distance-based anomaly detector together with an appropriate template construction method leading to more realistic and accurate results. We validated our approach with ten standard datasets and compared the performance with 50 state-of-the-art anomaly detectors. In our consideration, recent anomaly detectors have been re-evaluated in the same experimental setting for sound comparison. An analysis of variance (ANOVA) was conducted to compare the performance of our approach to those detectors in both desktops and recent smartphones. This study provides an in-depth understanding of each detector's performance which will aid in the design of efficient KD-based access control in the next generation of smart devices and applications.]]>
10.1504/IJBM.2024.137082
International Journal of Biometrics, Vol. 16, No. 2 (2024) pp. 133 - 157
Soumen Roy
Devadatta Sinha
Rajat Kumar Pal
Utpal Roy
Department of Computer Science and Engineering, University of Calcutta, Acharya Prafulla Chandra Roy Siksha Prangan, JD-2, Sector †III, Saltlake City, Kolkata †700106, India ' Department of Computer Science and Engineering, University of Calcutta, Acharya Prafulla Chandra Roy Siksha Prangan, JD-2, Sector †III, Saltlake City, Kolkata †700106, India ' Department of Computer Science and Engineering, University of Calcutta, Acharya Prafulla Chandra Roy Siksha Prangan, JD-2, Sector †III, Saltlake City, Kolkata †700106, India ' Department of Computer and System Sciences, Visva-Bharati University, Santiniketan †731235, India
access control
anomaly detection
keystroke dynamics
static authentication
template formation
template adaptation
touch dynamics
user authentication
2024-03-01T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
16
2
133
157
2024-03-01T23:20:50-05:00
-
A comparative study on friction ridge pore features of males and females
http://www.inderscience.com/link.php?id=137089
The sweat pores on the epidermal ridges of fingertips are unique and they are employed in personal identification. This study aimed to observe and analyse the pores within the left thumbprints of 50 individuals to find out whether there were any sex-related changes in the features of the pores. There was a significant (<i>p</i> < 0.05) difference in the average number of closed pores in males (53.60 ± 40.52) than that in females (81.60 ± 38.43). The average number of pores per 25 mm square was more frequent in females (156.12 ± 68.41) than males (105.12 ± 77.47). The difference in the distribution of pores per 5 mm of ridge length was found significant (<i>p</i> < 0.05) between males and females. The third-level features like type, shape, and frequency of the pores of males and females can be used as a presumptive indicator to determine the sex from fingerprints.
A comparative study on friction ridge pore features of males and females
C.D. Anjana; C.V. Priyatha; M.S. Siva Prasad
International Journal of Biometrics, Vol. 16, No. 2 (2024) pp. 158 - 175
The sweat pores on the epidermal ridges of fingertips are unique and they are employed in personal identification. This study aimed to observe and analyse the pores within the left thumbprints of 50 individuals to find out whether there were any sex-related changes in the features of the pores. There was a significant (<i>p</i> < 0.05) difference in the average number of closed pores in males (53.60 ± 40.52) than that in females (81.60 ± 38.43). The average number of pores per 25 mm square was more frequent in females (156.12 ± 68.41) than males (105.12 ± 77.47). The difference in the distribution of pores per 5 mm of ridge length was found significant (<i>p</i> < 0.05) between males and females. The third-level features like type, shape, and frequency of the pores of males and females can be used as a presumptive indicator to determine the sex from fingerprints.]]>
10.1504/IJBM.2024.137089
International Journal of Biometrics, Vol. 16, No. 2 (2024) pp. 158 - 175
C.D. Anjana
C.V. Priyatha
M.S. Siva Prasad
Department of Life Sciences, University of Calicut, Kerala Police Academy, Thrissur-680631, Kerala, India ' Department of Zoology, St. Thomas College (Autonomous), Thrissur- 680001, Kerala, India ' Department of Life Sciences, University of Calicut, Kerala Police Academy, Thrissur-680631, Kerala, India
fingerprints
poroscopy
AFRS
personal identification
sex difference
2024-03-01T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
16
2
158
175
2024-03-01T23:20:50-05:00
-
A minutiae-based method to store and compare fingerprints
http://www.inderscience.com/link.php?id=137101
Biometrics refers to certain physical or behavioural characteristics that are unique to every person. Biometrics, including fingerprints, are used for the measurement and analysis of biological data for identification purposes. This paper presents a new method to extract and compare fingerprint biometrics based on minutiae features. Only two reference minutiae are used to enhance the efficiency of the verification process with no need to match all the combinations of the extracted minutiae from the intellectual reader with the reference minutiae in the alignment algorithm. The method is implemented and tested with an average decrease of 80% in the number of combinations required to be matched with the reference minutiae when two reference minutiae points are used instead of one to align and match fingerprints.
A minutiae-based method to store and compare fingerprints
Eiman A. Alhamad; Mohammed S. Al Logmani; Abdullah T. Al-Essa; Mohammad Hammoudeh
International Journal of Biometrics, Vol. 16, No. 2 (2024) pp. 176 - 194
Biometrics refers to certain physical or behavioural characteristics that are unique to every person. Biometrics, including fingerprints, are used for the measurement and analysis of biological data for identification purposes. This paper presents a new method to extract and compare fingerprint biometrics based on minutiae features. Only two reference minutiae are used to enhance the efficiency of the verification process with no need to match all the combinations of the extracted minutiae from the intellectual reader with the reference minutiae in the alignment algorithm. The method is implemented and tested with an average decrease of 80% in the number of combinations required to be matched with the reference minutiae when two reference minutiae points are used instead of one to align and match fingerprints.]]>
10.1504/IJBM.2024.137101
International Journal of Biometrics, Vol. 16, No. 2 (2024) pp. 176 - 194
Eiman A. Alhamad
Mohammed S. Al Logmani
Abdullah T. Al-Essa
Mohammad Hammoudeh
Saudi Aramco, Dhahran, Saudi Arabia ' Saudi Aramco, Dhahran, Saudi Arabia ' Saudi Aramco, Dhahran, Saudi Arabia ' Information and Computer Science Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
biometrics
authentication
fingerprint
minutiae
2024-03-01T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
16
2
176
194
2024-03-01T23:20:50-05:00
-
Latent fingerprint segmentation using multi-scale attention U-Net
http://www.inderscience.com/link.php?id=137070
Latent fingerprints are the fingerprints lifted from crime scene surfaces. Segmentation of latent fingerprints from the background is an important preprocessing task which is challenging due to the poor quality of the fingerprints. Though fingerprint segmentation approaches based on their orientation and frequency are reported in the literature, they could not adequately address the problem. We propose a latent fingerprint segmentation model based on the U-Net attention network in this work. We added the Atrous Spatial Pyramid Pooling (ASPP) layer to the network to facilitate multi-scale fingerprint segmentation. Our approach could effectively segment the latent fingerprint region from the background and even detect occluded and partial fingerprints with simple network architecture. To evaluate the performance, we have compared our results with the manual ground truth using NIST SD27A dataset. Our segmentation model has improved matching accuracy on the NIST SD27A dataset.
Latent fingerprint segmentation using multi-scale attention U-Net
P. Akhila; Shashidhar G. Koolagudi
International Journal of Biometrics, Vol. 16, No. 2 (2024) pp. 195 - 215
Latent fingerprints are the fingerprints lifted from crime scene surfaces. Segmentation of latent fingerprints from the background is an important preprocessing task which is challenging due to the poor quality of the fingerprints. Though fingerprint segmentation approaches based on their orientation and frequency are reported in the literature, they could not adequately address the problem. We propose a latent fingerprint segmentation model based on the U-Net attention network in this work. We added the Atrous Spatial Pyramid Pooling (ASPP) layer to the network to facilitate multi-scale fingerprint segmentation. Our approach could effectively segment the latent fingerprint region from the background and even detect occluded and partial fingerprints with simple network architecture. To evaluate the performance, we have compared our results with the manual ground truth using NIST SD27A dataset. Our segmentation model has improved matching accuracy on the NIST SD27A dataset.]]>
10.1504/IJBM.2024.137070
International Journal of Biometrics, Vol. 16, No. 2 (2024) pp. 195 - 215
P. Akhila
Shashidhar G. Koolagudi
Department of Computer Science and Engineering, National Institute of Technology Karnataka, India ' Department of Computer Science and Engineering, National Institute of Technology Karnataka, India
latent fingerprint segmentation? U-Net? attention? weighted cross entropy? multi-scale
2024-03-01T23:20:50-05:00
Copyright © 2024 Inderscience Enterprises Ltd.
16
2
195
215
2024-03-01T23:20:50-05:00