A Cancelable Biometric System Based on Deep Style Transfer and Symmetry Check for Double-Phase User Authentication

The field of forensic science is experiencing significant growth, largely driven by the increasing integration of holographic and immersive technologies, along with their associated head-mounted displays. These immersive systems have become increasingly vital in resolving critical crimes as they facilitate communication, interaction, and collaboration. Given the sensitive nature of their work, crime investigators require substantial technical support. There is a pressing need for accurate documentation and archiving of crime scenes, which can be addressed by leveraging 3D scanned scenes to accurately represent evidence and expected scenarios. This study aims to develop an enhanced AR. system that can be deployed on hologram facilities such as the Microsoft HoloLens. The proposed system encompasses two main approaches, namely image classification and image segmentation. Image classification utilizes various deep learning models, including lightweight convolutional neural networks (CNNs) and convolutional Long-Short Term Memory (ConvLSTM). Additionally, the image segmentation approach is based on the fuzzy active contour model (FACM). The effectiveness of the proposed system was evaluated for both classification and segmentation tasks, utilizing metrics such as accuracy, sensitivity, precision, and F1 score. The simulation results indicate that the proposed system achieved a 99% accuracy rate in classification and segmentation tasks, positioning it as an effective solution for detecting bloodstain patterns in AR applications.

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A Proposed Cancelable Biometrical Recognition System (CBRS) Based on Developed Hénon Chaotic-Map

El-aziem,

Abdelhafeez,

Soliman

2024

Wireless Pers Commun

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Nowadays, human biometrics are widely used in authentication systems. In reaction to violent attacks, cancelable biometric patterns are developed from the original templates to increase the security level of biometric characteristics. This study proposes a solution for a cancelable biometrical recognition system (CBRS) based on the created Hénon chaotic-map idea, which increases key space and hence privacy. The suggested CBRS system ensures that the original biometric traits are updated and encrypted before they are saved in the database, protecting them from unwanted cyber-attacks. It makes efficient encryption of face biometric templates possible. The extraction of biometric characteristics is the first step in this design. Following that, the obtained biometric characteristics are encrypted using the suggested model, which causes pixel confusion and diffusion by developing a Henon chaotic map with variable block sizes at different modes of operation. Various face biometrics datasets were used to test the proposed approach. Various metrics, including security and statistical analyses, demonstrate the effectiveness of the approach, including histogram analysis, correlation coefficient analysis, maximum deviation factor analysis, irregular deviation factor analysis, number of pixels change rate analysis, unified average changing intensity analysis, time analysis, and key space analysis. Furthermore, the performance of the proposed approach was assessed using the receiver operating characteristic curve, which was constructed to assess the system's performance. Results of the analysis show that the suggested technique is very effective, resilient, and dependable, as evidenced by its great performance across diverse recognition databases when compared to traditional and modern algorithms, hence improving the security and reliability of biometric-based access management. The proposed method yields an average AROC of around 1, a correlation coefficient of about 0.00013, and an entropy close to one.

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A Cancelable Biometric System Based on Deep Style Transfer and Symmetry Check for Double-Phase User Authentication

Cited by 5 publications

References 37 publications

Quaternion double random phase encoding for privacy-preserving cancelable biometrics

Quaternion double random phase encoding for privacy-preserving cancelable biometrics

An efficient image classification and segmentation method for crime investigation applications

A Proposed Cancelable Biometrical Recognition System (CBRS) Based on Developed Hénon Chaotic-Map

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