Search for: All records

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. 

Limited data availability is a challenging problem in the latent fingerprint domain. Synthetically generated fingerprints are vital for training data-hungry neural network-based algorithms. Conventional methods distort clean fingerprints to generate synthetic latent fingerprints. We propose a simple and effective approach using style transfer and image blending to synthesize realistic latent fingerprints. Our evaluation criteria and experiments demonstrate that the generated synthetic latent fingerprints preserve the identity information from the input contact- based fingerprints while possessing similar characteristics as real latent fingerprints. Additionally, we show that the generated fingerprints exhibit several qualities and styles, suggesting that the proposed method can generate multiple samples from a single fingerprint. 

Using fingerphoto images acquired from mobile cameras, low-quality sensors, or crime scenes, it has become a challenge for automated identification systems to verify the identity due to various acquisition distortions. A significant type of photometric distortion that notably reduces the quality of a fingerphoto is the blurring of the image. This paper proposes a deep fingerphoto deblurring model to restore the ridge information degraded by the image blurring. As the core of our model, we utilize a conditional Generative Adversarial Network (cGAN) to learn the distribution of natural ridge patterns. We perform several modifications to enhance the quality of the reconstructed (deblurred) fingerphotos by our proposed model. First, we develop a multi-stage GAN to learn the ridge distribution in a coarse-to-fine framework. This framework enables the model to maintain the consistency of the ridge deblurring process at different resolutions. Second, we propose a guided attention module that helps the generator to focus mainly on blurred regions. Third, we incorporate a deep fingerphoto verifier as an auxiliary adaptive loss function to force the generator to preserve the ID information during the deblurring process. Finally, we evaluate the effectiveness of the proposed model through extensive experiments on multiple public fingerphoto datasets as well as real-world blurred fingerphotos. In particular, our method achieves 5.2 dB, 8.7%, and 7.6% improvement in PSNR, AUC, and EER, respectively, compared to a state-of-the-art deblurring method. 

Contactless fingerprints have continued to grow interoperability as a faster and more convenient replacement for contact fingerprints, and with covid-19 now starting to be a past event the need for hygienic alternatives has only grown after the sudden focus during the pandemic. Though, past works have shown issues with the interoperability of contactless prints from both kiosk devices and phone fingerprint collection apps. The focus of the paper is the evaluation of match performance between contact and contactless fingerprints, and the evaluation of match score bias based on skin demographics. AUC results indicate contactless match performance is as good as contact fingerprints, while phone contactless fingerprints fall short. Additionally, bias found for melanin showed specific ranges affected in both low melanin values and high melanin values. 

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. 

By combining two or more face images of look-alikes, morphed face images are generated to fool Facial Recognition Systems (FRS) into falsely accepting multiple people, leading to failures in security systems. Despite several attempts in the literature, finding pairs of bona fide faces to generate the morphed images is still a challenging problem. In this paper, we morph identical twin pairs to generate extremely difficult morphs for FRS. We first explore three methods of morphed face generation, GAN-based, landmark-based, and a wavelet-based morphing approach. We leverage these methods to generate morphs from the identical twin pairs that retain high similarity to both subjects while resulting in minimal artifacts in the visual domain. To further improve the difficulty of recognizing morphed face images, we perform an ablation study to apply adversarial perturbation to the morphs such that they cannot be detected by trained morph classifiers. The evaluation of the generated identical twin-morphed dataset is performed in terms of vulnerability analysis and presentation attack error rates.