Biometric Truth Map Authentication for Fraud and Damage Tolerance
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Solution Overview
Problem
Existing biometric identification and authentication systems are vulnerable to fraud attempts using decoys and are unreliable in cases where the body part is damaged, leading to reduced security and false negatives.
Innovation Solution
A biometric identification method that uses a truth map to validate the authenticity of body parts by associating each image portion with a truthfulness value, and calculates matching scores considering only genuine areas, thereby enhancing fraud detection and maintaining identification accuracy even with damaged body parts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image analysis methods are used to detect fraud, then fraud detection capability is improved, but robustness against sophisticated fraud is insufficient
Solution Approach 1:
The patent segments the biometric image into multiple regions and analyzes each region separately using a truth map. This segmentation allows the system to identify which parts of the image are genuine and which are fraudulent, thereby improving robustness against sophisticated fraud attempts while maintaining fraud detection capability.
Solution Approach 2:
The patent applies different quality assessments to different regions of the biometric image by generating a truth map with local truthfulness values. This local quality approach enables the system to distinguish between genuine and fraudulent regions, improving both fraud detection precision and reliability against sophisticated attacks.
2Measurement precision
If decision thresholds are set to validate identification in cases of body part degradation, then identification accuracy for damaged body parts is improved, but security is reduced due to validation of fraudulent acquisitions
Solution Approach 1:
The patent segments the biometric image into genuine and fraudulent regions using a truth map, allowing separate analysis of each region. This enables the system to validate identification based only on genuine regions, maintaining security while accommodating damaged body parts by ignoring degraded or fraudulent areas.
Solution Approach 2:
The patent applies local quality assessment through truthfulness values assigned to different image regions. This allows the system to differentiate between genuine biometric data and degraded or fraudulent areas, enabling accurate identification for damaged body parts without compromising security by validating fraudulent acquisitions.
3Reliability
If decision thresholds are set to invalidate fraudulent acquisitions, then system security is improved, but false negatives increase for genuinely damaged body parts
Solution Approach 1:
The patent segments the biometric image into genuine and fraudulent regions, enabling the system to apply different validation rules to each segment. This allows the system to maintain high security by invalidating fraudulent regions while preserving identification capability for genuinely damaged body parts by validating their genuine portions.
Solution Approach 2:
The patent uses local quality assessment through truthfulness values to differentiate between fraudulent and genuinely damaged regions. This enables the system to maintain security by rejecting fraudulent acquisitions while avoiding false negatives for genuinely damaged body parts, as the genuine portions can still be validated.
4Measurement precision
If physical parameters such as perspiration and pulse rate are measured, then fraud detection capability is improved, but device complexity and difficulty of setting thresholds increase
Solution Approach 1:
The patent extracts and removes the need for complex physical parameter measurement systems by focusing solely on image analysis with truth map generation. This extraction of the fraud detection function to purely visual methods reduces device complexity while maintaining fraud detection capability through advanced image processing.
Data Source
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AI summary
A biometric identification or authentication method is described. An image of a body area is obtained (S20). A truth map is obtained (S22) for said body area, said truth map associating with each portion of a set of portions of said image of a body area a probability that said portion belongs to a genuine body area. The image of the body area is then compared (S24) with a set of reference biometric data using the truth map. The identification or authentication of said body area is finally validated or invalidated (S26) in response to said comparison.