Template-Based Identity Document Classification with RANSAC Filtering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing template-based classification systems for identity documents suffer from high false detection rates, particularly in remote authentication scenarios, due to the reliance on optical checks which lack robustness in handling varied capture conditions and document orientations, leading to increased errors and resource wastage.
Innovation Solution
An iterative procedure using a RANSAC algorithm to generate hypotheses for document location and type, followed by filtering based on criteria such as well-conditionedness, geometric correctness, and area coverage, to reject poorly conditioned hypotheses, thereby reducing false detections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If template-based classification is used for identity documents, then processing speed is improved, but false detection rate increases
Solution Approach 1:
The patent applies preliminary action by performing hypothesis generation before full document analysis. The system generates multiple hypotheses about document location and type using template matching, then filters these hypotheses before proceeding to detailed analysis. This preliminary filtering step reduces false detections while maintaining processing speed by avoiding unnecessary detailed analysis of obviously incorrect hypotheses.
Solution Approach 2:
The patent segments the document classification process into distinct stages: hypothesis generation, hypothesis filtering, and detailed document analysis. By dividing the complex task into segments, the system can efficiently process documents at different levels of detail, maintaining high processing speed while improving reliability through multi-stage verification.
2Ease of operation
If optical checks are used for remote authentication, then convenience is improved, but robustness in varied capture conditions deteriorates
Solution Approach 1:
The patent applies parameter changes by adjusting the sensitivity and threshold parameters of the template matching algorithm based on capture conditions. The system modifies matching thresholds and hypothesis filtering criteria dynamically to accommodate varied lighting, angles, and document orientations, maintaining robustness while preserving the convenience of remote authentication.
Solution Approach 2:
The patent implements dynamics by making the classification system adaptive to different capture conditions. The hypothesis filtering mechanism dynamically adjusts its criteria based on the quality and characteristics of the input image, allowing the system to maintain high reliability across varied conditions while keeping the user experience convenient.
3Reliability
If hypothesis filtering criteria are applied, then false detections are reduced, but computational complexity increases
Solution Approach 1:
The patent applies partial action by implementing hypothesis filtering with selective thoroughness. The system applies simple filtering criteria initially to reject obviously incorrect hypotheses, then applies more complex analysis only to hypotheses that pass the initial filter. This approach reduces false detections while minimizing the increase in computational complexity by avoiding exhaustive analysis of all hypotheses.
Data Source
AI summary
Reducing false detections in template-based classification of identity documents. In an embodiment, an iterative procedure is used to generate one or more hypotheses for the location of a document in image data and a type of document in the image data based on a plurality of predefined models representing a plurality of types of documents. The one or more hypotheses are filtered by rejecting any hypothesis that is not well-conditioned according to one or more criteria. When a best hypothesis that satisfies a threshold remains after filtering the one or more hypotheses, the document in the image data is analyzed, and, when no hypothesis that satisfies the threshold remains after filtering the one or more hypotheses, the image data is rejected.