Local Line Directional Pattern for Palmprint Recognition

A novel cancelable FaceHashing technique based on non-invertible transformation with encryption and decryption template has been proposed in this paper. The proposed system has four components: face preprocessing, feature extraction, cancelable feature extraction followed by the classification, and encryption/decryption of cancelable face feature templates. During face preprocessing, the facial region of interest has been extracted out to speed the process for evaluating discriminant features. In feature extraction, some optimization techniques such as Sparse Representation Coding, Coordinate descent, and Block coordinates descent have been employed on facial descriptors to obtain the best representative of those descriptors. The representative descriptors are further arranged in a spatial pyramid matching structure to extract more discriminant and distinctive feature vectors. To preserve them, the existing BioHashing technique has been modified and extended to some higher levels of security attacks and the modified BioHashing technique computes a cancelable feature vector by the combined effect of the facial feature vector and the assigned token correspond to each user. The elements of computed cancelable feature vector are in a numeric form that has been employed to perform both verifications as well as identification task in online while the original facial feature vectors are kept offline either in hard drive or disc. Then, to enhance more security levels and also to preserve the cancelable face features, an RSA based encryption-decryption algorithm has been introduced. The proposed system has been tested using four benchmark face databases: CASIA-FACE-v5, IITK, CVL, and FERET, and performance are obtained as correct recognition rate and equal error rate. The performance are compared to the state-of-the-art methods for the superiority of the proposed feature extraction technique and individual performance analysis has been performed at all the security levels of the proposed Cancelable FaceHashing Technique. These comparisons show the superiority of the proposed system.

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A Novel Cancelable FaceHashing Technique Based on Non-invertible Transformation with Encryption and Decryption Template

The main role of cancellable biometric schemes is to protect the privacy of the enrolled users. The protected biometric data are generated by applying a parametrized transformation function to the original biometric data. Although cancellable biometric schemes achieve high security levels, they may degrade the recognition accuracy. One of the mostwidely used approaches to enhance the recognition accuracy in biometric systems is to combine several instances of the same biometric modality. In this paper, two multi-instance cancellable biometric schemes based on iris traits are presented. The iris biometric trait is used in both schemes because of the reliability and stability of iris traits compared to the other biometric traits. A generative adversarial network (GAN) is used as a transformation function for the biometric features. The first scheme is based on a pre-transformation feature-level fusion, where the binary features of multiple instances are concatenated and inputted to the transformation phase. On the other hand, the second scheme is based on a post-transformation feature-level fusion, where each instance is separately inputted to the transformation phase. Experiments conducted on the CASIA Iris-V3-Internal database confirm the high recognition accuracy of the two proposed schemes. Moreover, the security of the proposed schemes is analyzed, and their robustness against two well-known types of attacks is proven.

Multi-instance cancellable biometrics schemes based on generative adversarial network

The development of 5G networks has rapidly increased the use of Industrial Internet of Things (IIoT) devices for control, monitoring, and processing purposes. Biometric-based user authentication can prevent unauthorized access to IIoT devices, thereby safeguarding data security during production. However, most biometric authentication systems in the IIoT have no template protection, thus risking raw biometric data stored as templates in central databases or IIoT devices. Moreover, traditional biometric authentication faces slow, limited database holding capacity and data transmission problems. To address these issues, in this paper we propose a secure online fingerprint authentication system for IIoT devices over 5G networks. The core of the proposed system is the design of a cancelable fingerprint template, which protects original minutia features and provides privacy and security guarantee for both entity users and the message content transmitted between IIoT devices and the cloud server via 5G networks.Compared with state-of-the-art methods, the proposed authentication system shows competitive performance on six public fingerprint databases, while saving computational costs and achieving fast online matching.

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A Secure Online Fingerprint Authentication System for Industrial IoT Devices over 5G Networks

One factor cancellable fingerprint scheme based on novel minimum hash signature and secure extended feature vector

The traditional Cancellable biometrics scheme needs another key or token to generate the revocable template, which usually suffers from the token-stolen problem. To solve this problem, a one-factor cancellable palmprint biometric recognition scheme based on Orthogonal Index of Maximum (OIOM) hash and Minimum Signature Hash (MSH) is proposed to generate pseudonymous identifier. Firstly, to improve the efficiency and effectiveness, a parallel structure is designed to obtain Orthogonal Gaussian random projection(GRP)matrices, which is employed to generate the OIOM hash code. Secondly, a random binary string is XOR the binary OIOM hash code to construct the helper data and it is stored in the database. Meanwhile, this string is hashed by MSH to get the final pseudonymous identifier. Lastly, during matching stage, based on helper data and a palmprint images, another pseudonymous identifier is generated to recognition. This implies that, just one factor, a palmprint of user, is needed in the matching stage, and therefore the privacy of user is preserved. To evaluate the proposed scheme, PolyU database and touchless TJU database are used in experiments. The noninvertible, renewability, unlinkability and several security attacks of the proposed scheme are analyzed. The experiment results and analysis show that the proposed scheme has strong security on the basis of maintaining palmprint recognition performance.

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Xiyu Wang

Papers

One-Factor Cancellable Palmprint Recognition Scheme Based on OIOM and Minimum Signature Hash

One factor cancellable fingerprint scheme based on novel minimum hash signature and secure extended feature vector

One factor cancellable fingerprint scheme based on novel minimum hash signature and secure extended feature vector