Cancelable biometrics is a powerful remedy for information leakage caused by the extensive usage of unprotected biometric data. Current measures usually suffer from deteriorated accuracy, which is known as the security–performance tradeoff. Motivated by these concerns, in this article, a novel cancelable fingerprint approach, i.e., Indexing-Min–Max (IMM) hashing, is proposed to securely transform a fixed-length fingerprint feature vector to a discrete index hashed code. IMM hashing is essentially established upon the min–max hash and further strengthened by the integration of the partial Hadamard transform, which alleviates performance deterioration while maintaining a high security level. Extensive experiments on FVC2002 and FVC2004 fingerprint datasets coupled with comprehensive theoretical analyses demonstrate the favorable accuracy and strong anti-attack resilience of the proposed method. Besides, compared to the unprotected counterpart, the matching precision of the protected templates yields little accuracy loss or even improved performance, which means the security–performance tradeoff is well handled. Furthermore, IMM hashing also meets the unlinkability and revocability requisites of cancelable biometrics.

Representing fingerprint templates in binary form can provide outstanding merits compared to the conventional minutiae-based fingerprint recognition system. The existing fixed-length fingerprint feature extraction methods either suffer from redundant feature magnitude or lack of template security. In this letter, we present a compact (128 bytes) and cancelable fingerprint binary codes generation scheme which enables accurate and efficient comparison as well as high security. This binary representation is also available for advanced encryption schemes (e.g., fuzzy commitment). Specifically, a kernel learning-based real-valued fingerprint feature is converted into compact and cancelable binary code via one permutation hashing. A partial Haar transform is deployed to further strengthen the irreversibility of the whole system. Experimental results on six benchmark datasets FVC2002 and FVC2004 coupled with security analysis demonstrate the superiority of the proposed method compared with several state-of-the-arts.

It is challenging to theoretically predict the coefficient of thermal expansion (CTE) for binary crystals A_mB_n owing to the complexities of crystal structures and computational procedures. Herein, the Pearson feature selection method is utilized to identify nine key features associated closely with crystal structures, and a back-propagation neural network model with two hidden layers containing 24 and 15 neurons is adopted to achieve the optimal matching effect of CTE, which is specially optimized by the pelican optimization algorithm. Moreover, the black-box nature of the model is well elucidated by interpretability techniques of SHapley Additive exPlanations and Accumulated Local Effects, including the specific impact rules of each feature and the interaction effects between features on CTE. It is found that the feature of average bond length delivers the contribution of up to 27%, while low influence features serve an important function in increasing prediction accuracy. The findings demonstrate high efficiency and accuracy of the developed model for predicting CTE of binary crystals.

With the widespread application of biometric, unprotected biometric data is still at risk of serious security and privacy breaches. When large amounts of unprotected biometric data leak, cancelable biometric become a powerfully remedial measure. In this paper, we propose a new method to generate stable and cancelable face templates based on multiple partial Walsh transformations (MPWT) and Simhash. Firstly, multiple partial Walsh matrices generated with random external parameters perform projection transformation on the original real-valued face features, ensuring the irreversibility and unlinkability of the system. Subsequently, the projected features are transformed into discrete binary codes (protected templates) using Simhash. And the random permutation seed ensures the revocability of generated protected template. Furtherly, the protected templates have small storage space and is more suitable for fast comparison but also yields improvements in recognition accuracy compared with several state-of-the-arts. Numerous experiments on CASIA-WebFace, LFW, FEI, and Color FERET databases show that the protected templates are nearly identical to the unprotected ones in the comparison performance. The scheme also meets the requirements of non-invertibility, revocability, unlinkability, as well as resistance for various types of attacks like attacks via record multiplicity, false accepts, brute force and pre-image. Therefore, the proposed methodology strikes a balance between recognition accuracy and security.

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%.

Matching infrared (IR) facial probes against a gallery of visible light faces remains a challenge, especially when combined with cross-distance due to deteriorated quality of the IR data. In this paper, we study the scenario where visible light faces are acquired at a short standoff, while IR faces are long-range data. To address the issue of quality imbalance between the heterogeneous imagery, we propose to compensate it by upgrading the lower-quality IR faces. Specifically, this is realized through cascaded face enhancement that combines an existing denoising algorithm (BM3D) with a new deep-learning-based deblurring model we propose (named SVDFace). Different IR bands, short-wave infrared (SWIR) and near-infrared (NIR), as well as different standoffs, are involved in the experiments. Results show that, in all cases, our proposed approach for quality balancing yields improved recognition performance, which is especially effective when involving SWIR images at a longer standoff. Our approach outperforms another easy and straightforward downgrading approach. The cascaded face enhancement structure is also shown to be beneficial and necessary. Finally, inspired by the singular value decomposition (SVD) theory, the proposed deblurring model of SVDFace is succinct, efficient and interpretable in structure. It is proven to be advantageous over traditional deblurring algorithms as well as state-of-the-art deep-learning-based deblurring algorithms.