LFLDNet: Lightweight Fingerprint Liveness Detection Based on ResNet and Transformer.

With the rapid development of fingerprint recognition systems, fingerprint liveness detection is gradually becoming regarded as the main countermeasure to protect the fingerprint identification system from spoofing attacks. Convolutional neural networks have shown great potential in fingerprint liveness detection. However, the generalization ability of the deep network model for unknown materials, and the computational complexity of the network, need to be further improved. A new lightweight fingerprint liveness detection network is here proposed to distinguish fake fingerprints from real ones. The method includes mainly foreground extraction, fingerprint image blocking, style transfer based on CycleGan and an improved ResNet with multi-head self-attention mechanism. The proposed method can effectively extract ROI and obtain the end-to-end data structure, which increases the amount of data. For false fingerprints generated from unknown materials, the use of CycleGan network improves the model generalization ability. The introduction of Transformer with MHSA in the improved ResNet improves detection performance and reduces computing overhead. Experiments on the LivDet2011, LivDet2013 and LivDet2015 datasets showed that the proposed method achieves good results. For example, on the LivDet2015 dataset, our methods achieved an average classification error of 1.72 across all sensors, while significantly reducing network parameters, and the overall parameter number was only 0.83 M. At the same time, the experiment on small-area fingerprints yielded an accuracy of 95.27%.

Joint fully convolutional and graph convolutional networks for weakly-supervised segmentation of pathology images.

Tissue/region segmentation of pathology images is essential for quantitative analysis in digital pathology. Previous studies usually require full supervision (e.g., pixel-level annotation) which is challenging to acquire. In this paper, we propose a weakly-supervised model using joint Fully convolutional and Graph convolutional Networks (FGNet) for automated segmentation of pathology images. Instead of using pixel-wise annotations as supervision, we employ an image-level label (i.e., foreground proportion) as weakly-supervised information for training a unified convolutional model. Our FGNet consists of a feature extraction module (with a fully convolutional network) and a classification module (with a graph convolutional network). These two modules are connected via a dynamic superpixel operation, making the joint training possible. To achieve robust segmentation performance, we propose to use mutable numbers of superpixels for both training and inference. Besides, to achieve strict supervision, we employ an uncertainty range constraint in FGNet to reduce the negative effect of inaccurate image-level annotations. Compared with fully-supervised methods, the proposed FGNet achieves competitive segmentation results on three pathology image datasets (i.e., HER2, KI67, and H&E) for cancer region segmentation, suggesting the effectiveness of our method. The code is made publicly available at https://github.com/zhangjun001/FGNet.

Visualization of Latent Fingermarks by Enhanced Chemiluminescence Immunoassay and Pattern Recognition.

Herein we report the combination of enzyme-linked immunoassay and pattern recognition analysis for extracting both chemical and spatial information from latent fingermarks (LFMs). The development approach basically involves two steps, namely, specific recognition of protein and polypeptide secretions present in the ridge residues of LFMs by horseradish peroxidase (HRP)-labeled antibodies and the HRP-catalyzed chemiluminescent (CL) reaction between luminol and H2O2. The emitted light can spatially resolve the ridges, generating a bright image against the dark object surface for visualization of an LFM. Meanwhile, thanks to the molecular specificity of the immunoassay step, the emission also provides us additional information on the existence of specific substances in LFMs. The developed LFMs are further processed by a set of digital image processing procedures. Quantitative analysis based on minutia features shows that even poorly developed fingermarks can be matched successfully. This work offers the promise of facilitating cross-disciplinary studies between data-processing approaches and fingermark development techniques, such as the extraction of more information from LFM evidence, as well as the establishment of evaluation criteria for an enhancement technique.

Segmentation and Enhancement of Latent Fingerprints: A Coarse to Fine Ridge Structure Dictionary.

Latent fingerprint matching has played a critical role in identifying suspects and criminals. However, compared to rolled and plain fingerprint matching, latent identification accuracy is significantly lower due to complex background noise, poor ridge quality and overlapping structured noise in latent images. Accordingly, manual markup of various features (e.g., region of interest, singular points and minutiae) is typically necessary to extract reliable features from latents. To reduce this markup cost and to improve the consistency in feature markup, fully automatic and highly accurate ("lights-out" capability) latent matching algorithms are needed. In this paper, a dictionary-based approach is proposed for automatic latent segmentation and enhancement towards the goal of achieving "lights-out" latent identification systems. Given a latent fingerprint image, a total variation (TV) decomposition model with L1 fidelity regularization is used to remove piecewise-smooth background noise. The texture component image obtained from the decomposition of latent image is divided into overlapping patches. Ridge structure dictionary, which is learnt from a set of high quality ridge patches, is then used to restore ridge structure in these latent patches. The ridge quality of a patch, which is used for latent segmentation, is defined as the structural similarity between the patch and its reconstruction. Orientation and frequency fields, which are used for latent enhancement, are then extracted from the reconstructed patch. To balance robustness and accuracy, a coarse to fine strategy is proposed. Experimental results on two latent fingerprint databases (i.e., NIST SD27 and WVU DB) show that the proposed algorithm outperforms the state-of-the-art segmentation and enhancement algorithms and boosts the performance of a state-of-the-art commercial latent matcher.

Latent Fingerprint Matching: Performance Gain via Feedback from Exemplar Prints.

Latent fingerprints serve as an important source of forensic evidence in a court of law. Automatic matching of latent fingerprints to rolled/plain (exemplar) fingerprints with high accuracy is quite vital for such applications. However, latent impressions are typically of poor quality with complex background noise which makes feature extraction and matching of latents a significantly challenging problem. We propose incorporating top-down information or feedback from an exemplar to refine the features extracted from a latent for improving latent matching accuracy. The refined latent features (e.g. ridge orientation and frequency), after feedback, are used to re-match the latent to the top K candidate exemplars returned by the baseline matcher and resort the candidate list. The contributions of this research include: (i) devising systemic ways to use information in exemplars for latent feature refinement, (ii) developing a feedback paradigm which can be wrapped around any latent matcher for improving its matching performance, and (iii) determining when feedback is actually necessary to improve latent matching accuracy. Experimental results show that integrating the proposed feedback paradigm with a state-of-the-art latent matcher improves its identification accuracy by 0.5-3.5 percent for NIST SD27 and WVU latent databases against a background database of 100k exemplars.

A coarse to fine minutiae-based latent palmprint matching.

With the availability of live-scan palmprint technology, high resolution palmprint recognition has started to receive significant attention in forensics and law enforcement. In forensic applications, latent palmprints provide critical evidence as it is estimated that about 30 percent of the latents recovered at crime scenes are those of palms. Most of the available high-resolution palmprint matching algorithms essentially follow the minutiae-based fingerprint matching strategy. Considering the large number of minutiae (about 1,000 minutiae in a full palmprint compared to about 100 minutiae in a rolled fingerprint) and large area of foreground region in full palmprints, novel strategies need to be developed for efficient and robust latent palmprint matching. In this paper, a coarse to fine matching strategy based on minutiae clustering and minutiae match propagation is designed specifically for palmprint matching. To deal with the large number of minutiae, a local feature-based minutiae clustering algorithm is designed to cluster minutiae into several groups such that minutiae belonging to the same group have similar local characteristics. The coarse matching is then performed within each cluster to establish initial minutiae correspondences between two palmprints. Starting with each initial correspondence, a minutiae match propagation algorithm searches for mated minutiae in the full palmprint. The proposed palmprint matching algorithm has been evaluated on a latent-to-full palmprint database consisting of 446 latents and 12,489 background full prints. The matching results show a rank-1 identification accuracy of 79.4 percent, which is significantly higher than the 60.8 percent identification accuracy of a state-of-the-art latent palmprint matching algorithm on the same latent database. The average computation time of our algorithm for a single latent-to-full match is about 141 ms for genuine match and 50 ms for impostor match, on a Windows XP desktop system with 2.2-GHz CPU and 1.00-GB RAM. The computation time of our algorithm is an order of magnitude faster than a previously published state-of-the-art-algorithm.