An official website of the United States government
Traditional fingerprint authentication requires the acquisition of data through touch-based specialized sensors. However, due to many hygienic concerns including the global spread of the COVID virus through contact with a surface has led to an increased interest in contactless fingerprint image acquisition methods. Matching fingerprints acquired using contactless imaging against contact-based images brings up the problem of performing cross modal fingerprint matching for identity verification. In this paper, we propose a cost-effective, highly accurate and secure end-to-end contactless fingerprint recognition solution. The proposed framework first segments the finger region from an image scan of the hand using a mobile phone camera. For this purpose, we developed a cross-platform mobile application for fingerprint enrollment, verification, and authentication keeping security, robustness, and accessibility in mind. The segmented finger images go through fingerprint enhancement to highlight discriminative ridge-based features. A novel deep convolutional network is proposed to learn a representation from the enhanced images based on the optimization of various losses. The proposed algorithms for each stage are evaluated on multiple publicly available contactless databases. Our matching accuracy and the associated security employed in the system establishes the strength of the proposed solution framework.
more »
« less
Contactless Fingerprint Recognition Using Deep Learning—A Systematic Review
Contactless fingerprint identification systems have been introduced to address the deficiencies of contact-based fingerprint systems. A number of studies have been reported regarding contactless fingerprint processing, including classical image processing, the machine-learning pipeline, and a number of deep-learning-based algorithms. The deep-learning-based methods were reported to have higher accuracies than their counterparts. This study was thus motivated to present a systematic review of these successes and the reported limitations. Three methods were researched for this review: (i) the finger photo capture method and corresponding image sensors, (ii) the classical preprocessing method to prepare a finger image for a recognition task, and (iii) the deep-learning approach for contactless fingerprint recognition. Eight scientific articles were identified that matched all inclusion and exclusion criteria. Based on inferences from this review, we have discussed how deep learning methods could benefit the field of biometrics and the potential gaps that deep-learning approaches need to address for real-world biometric applications.
more »
« less
- Award ID(s):
- 1650503
- PAR ID:
- 10395541
- Date Published:
- Journal Name:
- Journal of Cybersecurity and Privacy
- Volume:
- 2
- Issue:
- 3
- ISSN:
- 2624-800X
- Page Range / eLocation ID:
- 714 to 730
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Deep learning models have demonstrated significant advantages over traditional algorithms in image processing tasks like object detection. However, a large amount of data are needed to train such deep networks, which limits their application to tasks such as biometric recognition that require more training samples for each class (i.e., each individual). Researchers developing such complex systems rely on real biometric data, which raises privacy concerns and is restricted by the availability of extensive, varied datasets. This paper proposes a generative adversarial network (GAN)-based solution to produce training data (palm images) for improved biometric (palmprint-based) recognition systems. We investigate the performance of the most recent StyleGAN models in generating a thorough contactless palm image dataset for application in biometric research. Training on publicly available H-PolyU and IIDT palmprint databases, a total of 4839 images were generated using StyleGAN models. SIFT (Scale-Invariant Feature Transform) was used to find uniqueness and features at different sizes and angles, which showed a similarity score of 16.12% with the most recent StyleGAN3-based model. For the regions of interest (ROIs) in both the palm and finger, the average similarity scores were 17.85%. We present the Frechet Inception Distance (FID) of the proposed model, which achieved a 16.1 score, demonstrating significant performance. These results demonstrated StyleGAN as effective in producing unique synthetic biometric images.more » « less
-
The match performance of contactless fingerprint probes compared to contact-based galleries has increased accuracy. This performance, along with convenience of use, is encouraging the utilization of contactless fingerprint collection methods. However, issues with differential performance for different demographics may still exist. Past works focused mainly on the interoperability of contactless prints with smartphone applications and kiosk devices. This paper focuses on the differential performance of genuine match scores based on the demographic of finger size, ridge density, and total ridge count. Distribution of genuine match scores shows a correlation between an increase in genuine match scores and these variables in contactless smartphone collection methods with the largest correlation appearing in finger size.more » « less
-
Finger photo recognition represents a promising touchless technology that offers portable and hygienic authentication solutions in smartphones, eliminating physical contact. Public spaces, such as banks and staff-less stores, benefit from contactless authentication considering the current public health sphere. The user captures the image of their own finger by using the camera integrated in a mobile device. Although recent research has pushed boundaries of finger photo matching, the security of this biometric methodology still represents a concern. Existing systems have been proven to be vulnerable to print attacks by presenting a color paper-printout in front of the camera and photo attacks that consist of displaying the original image in front of the capturing device. This paper aims to improve the performance of finger photo presentation attack detection (PAD) algorithms by investigating deep fusion strategies to combine deep representations obtained from different color spaces. In this work, spoofness is described by combining different color models. The proposed framework integrates multiple convolutional neural networks (CNNs), each trained using patches extracted from a specific color model and centered around minutiae points. Experiments were carried out on a publicly available database of spoofed finger photos obtained from the IIITD Smartphone Finger photo Database with spoof data, including printouts and various display attacks. The results show that deep fusion of the best color models improved the robustness of the PAD system and competed with the state-of-the-art.more » « less
-
Interoperability between contact to contactless images in fingerprint matching is a key factor in the success of contactless fingerprinting devices, which have recently witnessed an increasing demand for biometric authentication. However, due to the presence of perspective distortion and the absence of elastic deformation in contactless fingerphotos, direct matching between contactless fingerprint probe images and legacy contact-based gallery images produces a low accuracy. In this paper, to improve interoperability, we propose a coupled deep learning framework that consists of two Conditional Generative Adversarial Networks. Generative modeling is employed to find a projection that maximizes the pairwise correlation between these two domains in a common latent embedding subspace. Extensive experiments on three challenging datasets demonstrate significant performance improvements over the state-of-the-art methods and two top-performing commercial off-the-shelf SDKs, i.e., Verifinger 12.0 and Innovatrics. We also achieve a high-performance gain by combining multiple fingers of the same subject using a score fusion model.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/jcp2030036
