Biometric Recognition Feature Fusion and Encryption
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Solution Overview
Problem
Biometric recognition systems face challenges such as noise artifacts in iris images, high variability between images, vulnerability to spoofing attacks, and slow authentication processes, which compromise identity security and efficiency in real-time applications.
Innovation Solution
A deep learning-based iris recognition system that preprocesses eyeball images to extract iris and periocular features, uses multi-algorithm and multi-biometric approaches, and employs feature-level fusion and cancelable biometrics for enhanced security and faster authentication, storing encrypted templates to prevent misuse.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional cryptographic algorithms are used for biometric authentication, then security is improved, but authentication speed deteriorates
Solution Approach 1:
The system segments the biometric authentication process into multiple stages: feature extraction from iris and periocular regions, template generation using specific algorithms, and cryptographic processing. This segmentation allows optimization of each stage independently, maintaining security while improving overall speed.
Solution Approach 2:
The patent employs parameter changes by using different cryptographic algorithms (e.g., SHA-256, RSA) with varying key lengths and processing modes. By adjusting these parameters based on security requirements and performance needs, the system achieves a balance between security strength and authentication speed.
2Ease of manufacture
If conventional iris recognition methods are used, then implementation simplicity is improved, but performance under challenging imaging conditions deteriorates
Solution Approach 1:
The system merges iris recognition with periocular recognition into a unified biometric authentication framework. By combining multiple biometric traits (iris patterns and periocular features) into a single authentication decision, the system achieves robust performance under challenging imaging conditions while maintaining reasonable implementation complexity.
Solution Approach 2:
The patent creates a composite biometric template by fusing features from multiple sources (iris and periocular regions). This composite approach is analogous to composite materials in engineering, where combining different materials creates superior properties - here, combining different biometric modalities creates more reliable recognition performance.
3Ease of operation
If biometric data is stored for authentication, then authentication capability is improved, but vulnerability to data compromise deteriorates
Solution Approach 1:
The system performs preliminary actions by generating cryptographic hashes and encrypted templates of biometric data before storage. This preliminary processing ensures that even if stored data is compromised, the original biometric information cannot be reconstructed, thus protecting against data compromise while maintaining authentication capability.
Solution Approach 2:
The patent introduces cryptographic intermediaries (hash functions, encryption algorithms) between the raw biometric data and its stored representation. These intermediaries act as protective layers that enable authentication functionality while mitigating the harmful effects of potential data compromise.
Data Source
AI summary
The present disclosure provides a method for facilitating biometric recognition. Further, the method includes receiving, using a communication device, a biometric data from a user device. Further, the biometric data includes an eyeball image data. Further, the eyeball image data includes a periocular region image and an iris image. Further, the method includes processing, using a processing device, the biometric data using a machine learning model. Further, the method includes determining, using the processing device, an iris feature based on the processing. Further, the method includes determining, using the processing device, a periocular feature based on the processing. Further, the method includes concatenating, using the processing device, the iris feature and the periocular feature. Further, the method includes generating, using the processing device, an enrolled image data based on the concatenating. Further, the method includes storing, using a storage device, the enrolled image data in a database.