Fingerprint Presentation Attack Detection (PAD) is a crucial step in automatic fingerprint identification systems, which safeguards users from unauthorized malicious access. However, current presentation attack (i.e. spoof) techniques can forge intricate details of fingerprints (such as sweat holes), which makes the artifact evidence harder to detect. In this paper, we propose a novel PAD method from the perspective of decomposition to highlight the artifact evidence in each constituent element. Specifically, we utilize the fingerprint enhancement to decompose the fingerprint into the ridge region and the edge region. We observe that artifact evidence mainly exists in the gradient field within the ridge region, while it primarily resides in the spatial domain within the edge region. Then we propose an Orientation-Based Central Difference Convolution (OB-CDC) layer to prioritize gradient variations along the ridge direction. To further enhance robustness, we propose a Minutia Patches Random Rotation (MPRR) operation to disrupt the identity information of the fingerprint while preserving the artifact evidence. By integrating these techniques, we propose a two-stream network called Presentation-Attack-Detection-with-Region-Decomposition-Network (PADRD-Net) which integrates the processed feature of the ridge region and the edge region through a halfway fusion ResNet-18 structure. Experimental results on the LivDet 2021 dataset show that our proposed PADRD-Net can achieve 20.39% on BPCER@APCER = 1% and 87.12% on TDR@FDR = 1%, significantly outperforms the state-of-the-art. We also achieve outstanding performance in both the cross-sensor scenario and the cross-sensor and cross-material scenario. Extensive ablation studies and analysis experiments further indicate the effectiveness and robustness of our method.

Fingerprint recognition is a crucial biometric technology extensively used in identity verification, including areas like criminal investigations, security systems, and biometric authentication. This technology encounters greater challenges when dealing with incomplete fingerprint images, especially those with significant background noise or substantial portions of the fingerprint missing. Existing incomplete fingerprint recognition technologies struggle with extensive data loss, primarily due to the significant reduction and difficulty in extracting usable features from incomplete fingerprint images. Current image processing methods or deep learning models are unable to comprehensively reconstruct fingerprint features with limited information. To address these challenges, we introduce the Finger Recovery Transformer (FingerRT), an innovative network specifically designed for recovering incomplete fingerprint information. FingerRT can simultaneously complete ambient noise cancellation and fingerprint feature information recovery, resulting in a complete and clean fingerprint image. FingerRT combines the most critical feature information in fingerprints, directional field, and minutiae, as supervision information. FingerRT inherits the denoising ability of the fingerprint enhancement networks and the powerful generative ability of the Vision Transformer architecture, enabling high-quality and robust fingerprint information recovery. By imposing constraints at multiple levels, including fingerprint features, fingerprint images, and multi-stage generation, FingerRT can complete fingerprint information accurately and effectively. Experiments demonstrate that FingerRT significantly enhances fingerprint recognition accuracy after recovery across various fingerprint datasets, including rolled, snapped, and latent fingerprints.

The security of Automated Fingerprint Identification Systems (AFIS) heavily relies on the performance of the Fingerprint Presentation Attack Detection (FPAD) methods. However, the difficulty of FPAD lies in how to have strong robustness and generalization to unseen spoof fingerprints. To address this issue, we propose a novel FPAD framework with tailored Supervised Contrastive Learning (SupCon) and KNN-based OOD detection (KNN-OOD) method. We tailor the SupCon to better constrain the distribution of learned features by incorporating dynamic feature and label queues into SupCon and actively mining positive samples from the queues. In FPAD, we consider fingerprints with the same PAD label as intra-class, while those with different labels as inter-class. The tailored SupCon makes intra-class features more compact and inter-class features more dispersed. Utilizing the compact live fingerprint feature distribution, during the testing phase, we employ KNNOOD as an alternative to commonly used classification approaches. Since this approach does not rely on the distribution of trainset spoof fingerprints, it consistently achieves outstanding results even for unseen spoof fingerprints. Experiment results demonstrate that our proposed FPAD-SupCon framework achieves state-of-the-art performance on LivDet 2019 and LivDet 2021 datasets.

Latent fingerprint segmentation is a crucial process in contemporary biometric systems utilized in criminal investigations and security applications. Accurately segmenting the fingerprint region from the background noise and artifacts, which can be challenging due to the complexity of the surrounding environment, is the primary goal of this process. Although various methodologies, including binarization-based, texture-based, and deep learning-based segmentation approaches have been proposed, they are often limited by environmental noise and a scarcity of annotated data, resulting in a low segmentation accuracy rate. In this paper, we propose FingerSTR (Finger Segmentation Transformer), a fully Transformer-based latent fingerprint segmentation network, and introduce a new teacher-student training methodology to achieve more precise and robust segmentation results without requiring manual annotation. Based on experimental results of latent fingerprint database NIST SD27, FingerSTR surpasses both deep-learning algorithms and handcraft methods, achieving state-of-the-art performance in the latent fingerprint segmentation task.

Latent fingerprints play a vital role in forensic investigations. However, accurately estimating their orientation field can be challenging due to complex noise or overlapping fingerprint regions. In this paper, we propose a method to identify and correct these regions in the orientation field estimation. Specifically, our method comprises two networks: the first is an orientation field estimation network that outputs the initial orientation field, segment, and quality map, which determines the low-quality regions, including overlapping fingerprints and unclear ridge areas. The second network refills the orientation field in low-quality regions using inpainting techniques. This effectively handles unclear ridges and overlapping fingerprints, which can disrupt orientation field estimation. We assess our method using the NIST SD 27 dataset and demonstrate superior performance compared to existing state-of-the-art latent orientation field estimation methods, achieving the average root mean square deviation of 11.20.

With compact and discriminative fingerprint representation, fingerprint indexing can effectively reduce the search space and improve the efficiency in large-scale fingerprint identification. Previous fixed-length fingerprint representations do not combine the global and minutiae local information well, leading to unsatisfactory results. In this paper, we utilize an end-to-end network to extract Minutiae-aware fingerprint RepresentationS (MaRs) that consider both the global and minutiae local information. The proposed fingerprint representation is weighted aggregated by the output feature map of the network. We hope that not only the global pattern but also the matching minutia-centered regions are similar for the paired fingerprints. We impose constraints among proposed fingerprint representations and constraints among minutia local representations aggregated from each minutia-centered region. The latter constraint strengthens the similarity of matched minutiae local regions. Experimental results show that our minutiaeaware global representation outperforms previous methods in fingerprint indexing on two benchmarks and exhibits strong indexing robustness with a 100k expanded database.

Removing rain streaks from rainy images is necessary for many tasks in computer vision, such as object detection and recognition. It needs to address two mutually exclusive objectives: removing rain streaks and reserving realistic details. Balancing them is critical for de-raining methods. We propose an end-to-end network, called dual-task de-raining network (DTDN), consisting of two sub-networks: generative adversarial network (GAN) and convolutional neural network (CNN), to remove rain streaks via coordinating the two mutually exclusive objectives self-adaptively. DTDN-GAN is mainly used to remove structural rain streaks, and DTDN-CNN is designed to recover details in original images. We also design a training algorithm to train these two sub-networks of DTDN alternatively, which share same weights but use different training sets. We further enrich two existing datasets to approximate the distribution of real rain streaks. Experimental results show that our method outperforms several recent state-of-the-art methods, based on both benchmark testing datasets and real rainy images.