CBRW: a novel approach for cancelable biometric template generation based on 1-D random walk

Area of Science:

• Biometrics and Information Security
• Computer Vision and Image Processing
• Cryptography and Data Security

Background:

• Cancelable biometrics aim to secure original biometric data by transforming it into an irreversible domain.
• Existing methods for generating cancelable biometric templates have limitations.
• Novel approaches are needed to enhance the security and robustness of biometric templates.

Purpose of the Study:

• To propose two novel and simple cancelable biometric template generation methods based on Random Walk (CBRW).
• To evaluate the performance of the proposed CBRW methods against state-of-the-art techniques.
• To demonstrate the effectiveness of CBRW on both gray and color biometric datasets.

Main Methods:

• Development of two cancelable biometric template algorithms: CBRW-BitXOR and CBRW-BitCMP.
• Utilizing random walk and specific transformation steps to create irreversible biometric templates.
• Experimental validation on eight diverse public datasets (ear, iris, face) in gray and color.

Main Results:

• The proposed CBRW methods demonstrate superior performance compared to existing state-of-the-art techniques.
• Both qualitative and quantitative analyses confirm the effectiveness of the CBRW algorithms.
• CBRW methods show consistent performance improvements on both gray and color images.

Conclusions:

• The proposed CBRW-BitXOR and CBRW-BitCMP methods provide a secure and effective approach to cancelable biometric template generation.
• Random Walk-based methods offer a promising direction for enhancing biometric data security.
• The developed techniques are robust and perform well across various biometric modalities and image types.