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1. Multi Loss Fusion For Matching Smartphone Captured Contactless Finger Images

   https://doi.org/10.1109/WIFS53200.2021.9648393  

   Jawade, Bhavin; Agarwal, Akshay; Setlur, Srirangaraj; Ratha, Nalini (December 2021, 2021 IEEE International Workshop on Information Forensics and Security (WIFS))

   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. 

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2. Investigating the impact of demographic factors on contactless fingerprints interoperability

   https://doi.org/10.1109/BIOSIG55365.2022.9897061  

   Berti, Aeddon; Nasrabadi, Nasser; Dawson, Jeremy (September 2022, IEEE 21th International Conference of the Biometrics Special Interest Group (BIOSIG'22))

   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. 

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3. Contactless Fingerprint Recognition Using Deep Learning—A Systematic Review

   https://doi.org/10.3390/jcp2030036  

   Chowdhury, A M; Imtiaz, Masudul Haider (September 2022, Journal of Cybersecurity and Privacy)

   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. 

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4. Deep Coupled GAN-Based Score-Level Fusion for Multi-Finger Contact to Contactless Fingerprint Matching

   https://doi.org/10.1109/BIOSIG55365.2022.9897056  

   Hasan, Md Mahedi; Nasrabadi, Nasser; Dawson, Jeremy (September 2022, 2022 International Conference of the Biometrics Special Interest Group (BIOSIG))

   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. 

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5. An individual's skin stiffness predicts their tactile discrimination of compliance

   https://doi.org/10.1113/JP285271  

   Li, Bingxu; Gerling, Gregory J. (November 2023, The Journal of Physiology)

   AbstractIndividual differences in tactile acuity have been correlated with age, gender and finger size, whereas the role of the skin's stiffness has been underexplored. Using an approach to image the 3‐D deformation of the skin surface during contact with transparent elastic objects, we evaluate a cohort of 40 young participants, who present a diverse range of finger size, skin stiffness and fingerprint ridge breadth. The results indicate that skin stiffness generally correlates with finger size, although individuals with relatively softer skin can better discriminate compliant objects. Analysis of contact at the skin surface reveals that softer skin generates more prominent patterns of deformation, in particular greater rates of change in contact area, which correlate with higher rates of perceptual discrimination of compliance, regardless of finger size. Moreover, upon applying hyaluronic acid to soften individuals’ skin, we observe immediate, marked and systematic changes in skin deformation and consequent improvements in perceptual acuity in differentiating compliance. Together, the combination of 3‐D imaging of the skin surface, biomechanics measurements, multivariate regression and clustering, and psychophysical experiments show that subtle distinctions in skin stiffness modulate the mechanical signalling of touch and shape individual differences in perceptual acuity.Key pointsAlthough declines in tactile acuity with ageing are a function of multiple factors, for younger people, the current working hypothesis has been that smaller fingers are better at informing perceptual discrimination because of a higher density of neural afferents.To decouple relative impacts on tactile acuity of skin properties of finger size, skin stiffness, and fingerprint ridge breadth, we combined 3‐D imaging of skin surface deformation, biomechanical measurements, multivariate regression and clustering, and psychophysics.The results indicate that skin stiffness generally correlates with finger size, although it more robustly correlates with and predicts an individual's perceptual acuity.In particular, more elastic skin generates higher rates of deformation, which correlate with perceptual discrimination, shown most dramatically by softening each participant's skin with hyaluronic acid.In refining the current working hypothesis, we show the skin's stiffness strongly shapes the signalling of touch and modulates individual differences in perceptual acuity. 

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