With the widespread deployment of biometric authentication in Internet of Things (IoT) applications, protecting biometric templates while maintaining recognition performance and low resource consumption has become an important issue. However, existing revocable biometric template protection methods are mainly based on random projection, whose linear structure poses a reversibility risk when both transformation parameters and multiple protected templates are compromised. To address this issue, this paper proposes a revocable biometric template generation method, termed Weighted Absolute Value Transformation based on Random Bezier Matrices (WAVTRBM). The proposed method employs random Bezier matrices to preserve the discriminative information of biometric features during transformation, and introduces a random weight vector into the absolute value transformation to improve similarity preservation and matching performance. The method can generate both real-valued and binary protected templates, with the advantages of low storage overhead and fast computation, making it suitable for resource-constrained IoT devices and real-time authentication scenarios. Experiments on face, fingerprint, palmprint, and palm vein databases show that, compared with conventional random projection-based methods and existing absolute value transformation-based methods, the proposed method improves template security while maintaining good recognition performance and supporting efficient template generation and matching. Theoretical and experimental analyses further demonstrate that the proposed method satisfies the requirements of revocable biometrics and can resist various attacks.

With the rapid development of Internet of Things (IoT), biometric-based authentication systems have been widely used for access control. The wide application of biometric recognition systems has brought convenience but also raised privacy and security concerns. When unprotected templates are stolen, it will permanently leak the original biometric data. Therefore, it is important to ensure the security of biometric templates while meeting the real-time device requirements. Motivated by these issues, in this paper, we proposed a scheme based on partial Cake-cutting transformation and spherical hashing to generate cancelable binary face templates. Firstly, with external random parameters, partial Cake-cutting transformation is established to introduce randomness and preserve the relative distance similarity of face features. Then spherical hashing is utilized to encode the face features into protected binary codes. The protected template has the advantages of high entropy value, low storage consumption, and fast generation speed. Extensive experiments conducted on LFW, CFPW, and CASIA-FaceV5 databases along with theoretical analyses indicate that the proposed scheme shows good matching accuracy and strong resistance to various attacks. Besides, the protected templates can achieve equal or even better accuracy than the unprotected counterparts. Furthermore, the proposed scheme also satisfies the requirements of cancelable biometrics, i.e., irreversibility, revocability, and unlinkability.

Existing partial fingerprint matching methods use fingerprint ridge features and minutiae or employ algorithms like SIFT and A-KAZA to create new feature points that can replace minutiae for feature extraction and matching. While these methods have achieved some success in improving matching performance, they rely on manually designed rules for extracting local area features, which limits their accuracy and generalization capability. To address these limitations, this paper proposes a novel partial fingerprint matching algorithm that leverages Feature Similarity and Pre-training. Specifically, Feature Similarity is integrated into a deep learning-based model to emulate traditional partial fingerprint matching techniques. Additionally, Pre-training guides the model to learn subtle yet identity-discriminative features within partial fingerprints. Experimental results on partial fingerprint databases constructed from FVC2004 DB1, DB2, and DB3 show that our algorithm achieves low EER and ZeroFMR, outperforming several state-of-the-art matching methods.

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.

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.

The minutiae set defined by the ISO/IEC 19794‐2 is one of the prevalent feature used in fingerprint recognition systems. Unfortunately, such characteristic of unordered and variable‐sized minutiae information causes a restriction on the operation in some advanced template protection methods (e.g. fuzzy commitment), which usually require an ordered and fixed‐length binary feature representation as the system input. In this study, in order to simultaneously extend the application of fingerprint recognition and provide satisfactory system performance, the authors propose a novel fixed‐length bit‐string conversion framework based on spectral clustering and the proposed newly designed discriminative fingerprint representation called minutia vicinity combined feature (MVCF). The proposed method consists of three stages: (i) the extraction of MVCF, (ii) bit conversion via the spectral clustering algorithm, and (iii) matching. Benefiting from feature invariance, fixed‐length and bit‐oriented coding, merits such as fast matching and decent accuracy are well guaranteed. The performance evaluation is conducted on six publicly available benchmark data sets: FVC2002 DB1, DB2, DB3 and FVC2004 DB1, DB2, DB3 confirms the superiority of the proposed method and suggests the promise of migrating to some other domains (e.g., template protection).

Small‐sized fingerprint sensors, due to the convenience of integration, are widely used in many applications, especially on smart phones. However, the friction ridge information decreases with the reduction of the collected fingerprint area, resulting in degraded recognition performance. Mosaicking fingerprint impressions has been proved to be effective in boosting the recognition accuracy. Nonetheless, the minutiae‐based mosaicking methods do not work well when there is no sufficient number of minutiae in the overlapping area while existing minutia‐free mosaicking methods are not robust to distortion and result in low mosaicking accuracy. In this study, a novel minutia‐free mosaicking algorithm used the coarse‐to‐fine approach is proposed to obtain a larger fingerprint impression from a couple of small‐sized fingerprint impressions. It consists of three stages: an orientation field‐based coarse alignment, a ridge matching‐based fine alignment, and a nonlinear deformation correction with block‐correspondence Thin Plate Spline model. Experimental results on the XDfinger database demonstrate that the proposed method outperforms the other six mosaicking methods in terms of reject‐to‐fuse rate, registration accuracy, and verification performance. Specifically, in the verification scenario, the equal error rate is reduced from 1.98% of a single impression to 0.41% of two impressions mosaicked by the authors' method.

In recent years, palmprint recognition has made great progress and many methods have been put forward. The extraction of robust orientation features and finding efficient matching strategies are two key points for palmprint recognition. Traditional coding methods usually only use a dominant filter response to extract orientation features of palmprint images while not taking into account the other useful filter responses. Without increasing the number of filers, this study presents a modified Competitive Code to extract orientation features more accurately, which makes use of the relation between the filter responses. Besides, a distinctive extended eight‐pixel neighbourhood method is proposed to select the sample points for matching by extracting the local features. At the matching stage, an effective fusion matching scheme with a double‐layer image pyramid is designed to calculate the similarity between two palmprint images. Extensive experiments on four types of public palmprint databases show that the proposed method has excellent performance compared with the other state‐of‐the‐art algorithms.

Partial face recognition has been a problem of interest for more than a decade. Most of previous publications on partial face recognition assume intra spectral matching. Matching Short Wave Infrared (SWIR), Middle Wave Infrared (MWIR) or Near Infrared (NIR) images of partial face to a gallery of color images is a much more challenging task. The photometric properties of images in these four spectral bands are highly distinct. Because of the limited space - and also sufficient interest to this biometric - in this paper we present results of cross spectral matching applied to periocular regions. Equipped with a well developed automatic recognition algorithm for heterogeneous face, we demonstrate that the algorithm can be tuned and applied to periocular regions for a positive cross spectral matching of SWIR, MWIR and NIR periocular regions to visible periocular regions at short (1.5 m) and long (50 and 106 m) standoff distances. Our numerical analysis demonstrates the results of the matching. To the best of our knowledge, the performance evaluation presented in this paper is the first of its kind.