Customer Photograph Verification With Classification And Region Detection
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Solution Overview
Problem
Existing rental systems face inefficiencies due to customers uploading incorrect types of photographs and misaligned regions of interest, requiring manual intervention by customer service representatives, which is time-consuming.
Innovation Solution
A method and system using a pipeline of computer vision models, including a classification model, identification card detection model, and face detection model, to automatically classify and identify the region of interest in uploaded photographs, reducing the need for manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual verification of uploaded photographs is performed by customer service representatives, then accuracy of photo type verification is improved, but time consumption and operational efficiency deteriorate
Solution Approach 1:
The system enables self-service by implementing an automated classification model that independently verifies uploaded photographs without human intervention. The model automatically categorizes photos as headshots, driver's license fronts, or driver's license backs, eliminating the need for customer service representatives to manually review each photo while maintaining verification accuracy.
Solution Approach 2:
The patent replaces the mechanical system of manual photo verification by human representatives with an automated computer vision system. The classification model uses image processing algorithms to analyze and categorize uploaded photographs, substituting human visual inspection and decision-making with automated computational analysis.
2Ease of operation
If customers take their own photographs, then ease of operation is improved, but photo quality and region of interest alignment deteriorate
Solution Approach 1:
The system implements feedback by analyzing the uploaded photograph and automatically determining the region of interest. The classification model provides feedback about the detected photo type and identifies the relevant region (face for headshots, card details for license photos), enabling the system to adapt its verification process based on the actual content of uploaded images.
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting the region of interest parameters based on the classified photo type. For headshots, the system focuses on facial features; for driver's license fronts, it identifies both the card and embedded photo; for license backs, it locates the card region. This adaptive parameter adjustment ensures accurate verification despite variations in customer-uploaded photo quality.
Data Source
AI summary
A method and system are provided for automatically verifying an image uploaded to a computer system by a customer. The method includes generating an upload prompt to request a customer to upload an image of a headshot of the customer, a front of an identification card of the customer, or a back of an identification card of the customer. The uploaded image is automatically processed with a computer-implemented classification model to attempt to classify the uploaded image as a headshot, an identification card front, or an identification card back. An uploaded headshot is processed with a face detection model to return face bounding box for the image. An uploaded identification card front is processed with an identification card detection model to generate an identification card front bounding box for the uploaded image, and with the face detection model to return an identification card face bounding box for the identification card front. An uploaded identification card back is processed with the identification card detection model to return an identification card back bounding box for the uploaded image.