How Rapid ID Verification Enhances Alcohol Delivery Services
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Chapter 1: Understanding Rapid ID Checks
In the United States and Canada, it is common for delivery personnel to verify a customer's identity when alcohol is ordered. Additionally, you might encounter similar verification processes when submitting your ID for services requiring identity confirmation. For example, Uber riders must upload a government-issued ID for their verification process. This blog will explore how these real-time ID checks function behind the scenes and the steps involved once you click to capture an image of your ID.
To understand this process better, we will analyze how Uber executes real-time ID verification.
Section 1.1: The Components of ID Verification
The process of uploading an ID and receiving verification results can be broken down into four primary components:
- Document Image Collection
- Document Image Processing
- Results Evaluation
- Results Output
Subsection 1.1.1: Document Image Collection
During this initial phase, the user captures and uploads an image of their ID. Checks are conducted on the application side. For instance, Uber employs machine learning to identify common issues, such as:
- Missing ID in the image
- Image being cropped
- Unclear ID details
Section 1.2: Document Image Processing
At this stage, the uploaded document undergoes various procedures, including classification, transcription, and fraud detection. The type of ID—whether it’s a driver’s license, passport, or national ID—is determined to ensure that a valid form of identification has been provided. Once classified, critical information extraction follows, often utilizing Optical Character Recognition (OCR) to gather essential details like the date of birth or license number.
Chapter 2: Evaluating Results and Overcoming Challenges
The first video discusses the ID scanning process within the Spark Driver app, illustrating potential glitches and missing tips during verification.
Results Evaluation
After extracting the relevant information, a thorough analysis is conducted to verify the data’s accuracy. For example, when a customer orders alcohol, the extracted details are used to confirm that the individual meets the legal drinking age. Once verified, the data is saved in a verification table.
The second video provides insights for Instacart shoppers on how to navigate the complexities of delivering alcohol without legal issues.
Results Output
Once the user successfully passes all checks, an event is sent to the notification service to inform them of the verification outcome.
Challenges with Real-Time Document Verification
Real-time document verification presents several challenges, including:
- Low-Quality Images: Poor image quality can lead to verification failures due to issues such as overexposure or incomplete captures. To address this, Uber has developed a client-side machine learning model to detect and flag low-quality images before they are submitted.
- ID Variations: Different countries have various ID formats, and even within a country, multiple versions of the same ID may exist. The document processing must accurately identify and extract key fields from these diverse formats.
- Human-in-the-Loop: In cases where automated systems struggle to obtain acceptable image quality, human reviews are necessary for accurate verification.
Conclusion
This blog provided an overview of how modern technologies like machine learning and OCR contribute to real-time ID document verification. Despite challenges such as low-quality images and variations in ID formats, these systems are designed to ensure a seamless and secure experience for users.