Fingerprint Pore Detection: A Survey

This work presents the first survey on fingerprint pore detection. The survey provides a general overview of the field and discusses methods, datasets, and evaluation protocols. We also present a baseline method inspired on the state-of-the-art that implements a customizable Fully Convolutional Network, whose hyperparameters were tuned to achieve optimal pore detection rates. Finally, we also reimplementated three other approaches proposed in the literature for evaluation purposes. We have made the source code of (1) the baseline method, (2) the reimplemented approaches, and (3) the training and evaluation processes for two different datasets available to the public to attract more researchers to the field and to facilitate future comparisons under the same conditions. The code is available in the following repository: https://github.com/azimIbragimov/Fingerprint-Pore-Detection-A-Surve

Fingerprint Liveness Detection using Minutiae-Independent Dense Sampling of Local Patches

Fingerprint recognition and matching is a common form of user authentication. While a fingerprint is unique to each individual, authentication is vulnerable when an attacker can forge a copy of the fingerprint (spoof). To combat these spoofed fingerprints, spoof detection and liveness detection algorithms are currently being researched as countermeasures to this security vulnerability. This paper introduces a fingerprint anti-spoofing mechanism using machine learning.Comment: Submitted, peer-reviewed, accepted, and under publication with Springer Natur

DyFFPAD: Dynamic Fusion of Convolutional and Handcrafted Features for Fingerprint Presentation Attack Detection

Automatic fingerprint recognition systems suffer from the threat of presentation attacks due to their wide range of applications in areas including national borders and commercial applications. Presentation attacks can be performed by fabricating the fake fingerprint of a user with or without the intention of the subject. This paper presents a dynamic ensemble of deep learning and handcrafted features to detect presentation attacks in known-material and unknown-material protocols. The proposed model is a dynamic ensemble of deep CNN and handcrafted features empowered deep neural networks both of which learn their parameters together. The proposed presentation attack detection model, in this way, utilizes the capabilities of both classification techniques and exhibits better performance than their individual results. The proposed model's performance is validated using benchmark LivDet 2015, 2017, and 2019 databases, with an overall accuracy of 96.10\%, 96.49\%, and 95.99\% attained on them, respectively. The proposed model outperforms state-of-the-art methods in benchmark protocols of presentation attack detection in terms of classification accuracy.Comment: arXiv admin note: text overlap with arXiv:2305.0939

PGT-Net: Progressive Guided Multi-task Neural Network for Small-area Wet Fingerprint Denoising and Recognition

Fingerprint recognition on mobile devices is an important method for identity verification. However, real fingerprints usually contain sweat and moisture which leads to poor recognition performance. In addition, for rolling out slimmer and thinner phones, technology companies reduce the size of recognition sensors by embedding them with the power button. Therefore, the limited size of fingerprint data also increases the difficulty of recognition. Denoising the small-area wet fingerprint images to clean ones becomes crucial to improve recognition performance. In this paper, we propose an end-to-end trainable progressive guided multi-task neural network (PGT-Net). The PGT-Net includes a shared stage and specific multi-task stages, enabling the network to train binary and non-binary fingerprints sequentially. The binary information is regarded as guidance for output enhancement which is enriched with the ridge and valley details. Moreover, a novel residual scaling mechanism is introduced to stabilize the training process. Experiment results on the FW9395 and FT-lightnoised dataset provided by FocalTech shows that PGT-Net has promising performance on the wet-fingerprint denoising and significantly improves the fingerprint recognition rate (FRR). On the FT-lightnoised dataset, the FRR of fingerprint recognition can be declined from 17.75% to 4.47%. On the FW9395 dataset, the FRR of fingerprint recognition can be declined from 9.45% to 1.09%

RFDforFin: Robust Deep Forgery Detection for GAN-generated Fingerprint Images

With the rapid development of the image generation technologies, the malicious abuses of the GAN-generated fingerprint images poses a significant threat to the public safety in certain circumstances. Although the existing universal deep forgery detection approach can be applied to detect the fake fingerprint images, they are easily attacked and have poor robustness. Meanwhile, there is no specifically designed deep forgery detection method for fingerprint images. In this paper, we propose the first deep forgery detection approach for fingerprint images, which combines unique ridge features of fingerprint and generation artifacts of the GAN-generated images, to the best of our knowledge. Specifically, we firstly construct a ridge stream, which exploits the grayscale variations along the ridges to extract unique fingerprint-specific features. Then, we construct a generation artifact stream, in which the FFT-based spectrums of the input fingerprint images are exploited, to extract more robust generation artifact features. At last, the unique ridge features and generation artifact features are fused for binary classification (\textit{i.e.}, real or fake). Comprehensive experiments demonstrate that our proposed approach is effective and robust with low complexities.Comment: 10 pages, 8 figure

Recent Developments in Atomic Force Microscopy and Raman Spectroscopy for Materials Characterization

This book contains chapters that describe advanced atomic force microscopy (AFM) modes and Raman spectroscopy. It also provides an in-depth understanding of advanced AFM modes and Raman spectroscopy for characterizing various materials. This volume is a useful resource for a wide range of readers, including scientists, engineers, graduate students, postdoctoral fellows, and scientific professionals working in specialized fields such as AFM, photovoltaics, 2D materials, carbon nanotubes, nanomaterials, and Raman spectroscopy