Identity Document Image Pairs for Machine Learning Authenticity Detection
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Solution Overview
Problem
Existing methods for determining the authenticity of identity documents are inefficient, costly, and lack accuracy, particularly in detecting fraudulent identity documents used in remote transactions.
Innovation Solution
A method involving an electronic device that captures an image of an identity document, crops it to remove extraneous data, generates a gray scale image, and processes both images using a trained machine learning model to determine authenticity, enhancing accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review methods are used to verify identity documents, then accuracy can be maintained, but processing speed and scalability deteriorate significantly
Solution Approach 1:
The patent replaces manual mechanical review processes with an automated machine learning system. The MLM automatically analyzes identity document images, extracting features and determining authenticity without human intervention, thereby maintaining accuracy while dramatically improving processing speed and scalability.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between the identity document image and the authenticity determination. The MLM processes the image through multiple layers, extracting meaningful features and making decisions based on learned patterns, serving as an intelligent mediator that bridges manual review accuracy with automated efficiency.
2Productivity
If automated analysis methods are used to process identity documents, then processing speed improves, but accuracy and trustworthiness of results deteriorate
Solution Approach 1:
The patent transforms the identity document image into multiple dimensional representations including gray-scale images and feature vectors. By processing the image across different dimensions (color channels, spatial features, texture patterns), the MLM captures comprehensive information that improves accuracy while maintaining automated processing efficiency.
Solution Approach 2:
The patent segments the identity document image into multiple feature components that are processed independently by the MLM. The model extracts and analyzes different aspects such as text regions, security features, biometric data, and layout patterns separately, then integrates these segmented analyses to make an overall authenticity determination, thereby improving accuracy through detailed feature examination.
3Measurement precision
If comprehensive feature analysis is performed on identity documents, then detection accuracy improves, but computational complexity and cost increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing the identity document image before main analysis. The system converts the image to gray-scale, extracts key features, and prepares data structures in advance. This preliminary processing reduces the complexity of the main authentication algorithm while maintaining comprehensive analysis capability, thereby improving efficiency without sacrificing accuracy.
Data Source
AI summary
A method for determining authenticity of a document is provided that includes the step of capturing, by an electronic device, an image of a document. The document includes image data of a biometric modality of a user and informational data. Moreover, the method includes the steps of cropping the captured image data to include an image of the document only, removing the informational data from the cropped image, and generating a gray scale image from the cropped image. The gray scale image and the cropped image are a pair of images. Furthermore, the method includes the step of simultaneously processing, by a trained machine learning model (MLM) each image in the pair of images, to determine whether the identity document in the captured image is authentic.