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1. Fingerprint Distortion Rectification using Deep Convolutional Neural Networks

   Dabouei, Ali; Kazemi, Hadi; Iranmanesh, Seyed-Mehdi; Dawson, Jeremy; Nasrabadi, Nasser M (February 2018, The 11th IAPR International Conference on Biometrics (ICB 2018))

   Elastic distortion of fingerprints has a negative effect on the performance of fingerprint recognition systems. This negative effect brings inconvenience to users in authentication applications. However, in the negative recognition scenario where users may intentionally distort their fingerprints, this can be a serious problem since distortion will prevent recognition system from identifying malicious users. Current methods aimed at addressing this problem still have limitations. They are often not accurate because they estimate distortion parameters based on the ridge frequency map and orientation map of input samples, which are not reliable due to distortion. Secondly, they are not efficient and requiring significant computation time to rectify samples. In this paper, we develop a rectification model based on a Deep Convolutional Neural Network (DCNN) to accurately estimate distortion parameters from the input image. Using a comprehensive database of synthetic distorted samples, the DCNN learns to accurately estimate distortion bases ten times faster than the dictionary search methods used in the previous approaches. Evaluating the proposed method on public databases of distorted samples shows that it can significantly improve the matching performance of distorted samples. 

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2. FITS: Matching Camera Fingerprints Subject to Software Noise Pollution

   https://doi.org/10.1145/3576915.3616600  

   Liu, Liu; Fu, Xinwen; Chen, Xiaodong; Wang, Jianpeng; Ba, Zhongjie; Lin, Feng; Lu, Li; Ren, Kui (November 2023, CCS '23: Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security)

   Physically unclonable hardware fingerprints can be used for device authentication. The photo-response non-uniformity (PRNU) is the most reliable hardware fingerprint of digital cameras and can be conveniently extracted from images. However, we find image post-processing software may introduce extra noise into images. Part of this noise remains in the extracted PRNU fingerprints and is hard to be eliminated by traditional approaches, such as denoising filters. We define this noise as software noise, which pollutes PRNU fingerprints and interferes with authenticating a camera armed device. In this paper, we propose novel approaches for fingerprint matching, a critical step in device authentication, in the presence of software noise. We calculate the cross correlation between PRNU fingerprints of different cameras using a test statistic such as the Peak to Correlation Energy (PCE) so as to estimate software noise correlation. During fingerprint matching, we derive the ratio of the test statistic on two PRNU fingerprints of interest over the estimated software noise correlation. We denote this ratio as the fingerprint to software noise ratio (FITS), which allows us to detect the PRNU hardware noise correlation component in the test statistic for fingerprint matching. Extensive experiments over 10,000 images taken by more than 90 smartphones are conducted to validate our approaches, which outperform the state-of-the-art approaches significantly for polluted fingerprints. We are the first to study fingerprint matching with the existence of software noise. 

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3. 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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4. Synthetic Latent Fingerprint Generation Using Style Transfer

   https://doi.org/10.1109/BIOSIG58226.2023.10345974  

   Joshi, Amol S.; Dabouei, Ali; Nasrabadi, Nasser M.; Dawson, Jeremy (September 2023, 2023 International Conference of the Biometrics Special Interest Group (BIOSIG))

   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. 

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5. Are 2D fingerprints still valuable for drug discovery?

   https://doi.org/10.1039/D0CP00305K  

   Gao, Kaifu; Nguyen, Duc Duy; Sresht, Vishnu; Mathiowetz, Alan M.; Tu, Meihua; Wei, Guo-Wei (April 2020, Physical Chemistry Chemical Physics)

   Recently, molecular fingerprints extracted from three-dimensional (3D) structures using advanced mathematics, such as algebraic topology, differential geometry, and graph theory have been paired with efficient machine learning, especially deep learning algorithms to outperform other methods in drug discovery applications and competitions. This raises the question of whether classical 2D fingerprints are still valuable in computer-aided drug discovery. This work considers 23 datasets associated with four typical problems, namely protein–ligand binding, toxicity, solubility and partition coefficient to assess the performance of eight 2D fingerprints. Advanced machine learning algorithms including random forest, gradient boosted decision tree, single-task deep neural network and multitask deep neural network are employed to construct efficient 2D-fingerprint based models. Additionally, appropriate consensus models are built to further enhance the performance of 2D-fingerprint-based methods. It is demonstrated that 2D-fingerprint-based models perform as well as the state-of-the-art 3D structure-based models for the predictions of toxicity, solubility, partition coefficient and protein–ligand binding affinity based on only ligand information. However, 3D structure-based models outperform 2D fingerprint-based methods in complex-based protein–ligand binding affinity predictions. 

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