Advanced Face Search (1:N) with Didit's JS SDK

Proactive Fraud DetectionDidit's 1:N Face Search automatically compares new user biometrics against all previously verified identities, flagging potential duplicate accounts and blocklisted individuals in real-time.

Seamless JS SDK IntegrationDevelopers can easily embed advanced face search functionality into their web applications using Didit's intuitive JavaScript SDK, simplifying implementation and reducing development time.

Configurable Security ThresholdsTailor the sensitivity of face search matches with customizable similarity thresholds, allowing businesses to balance security needs with user experience and risk tolerance.

Didit's AI-Native AdvantageDidit provides an AI-native, modular identity platform with Free Core KYC, enabling businesses to deploy sophisticated biometric fraud prevention without setup fees, ensuring high accuracy and rapid results.

In today's digital landscape, robust identity verification is paramount for businesses across all sectors. As online interactions become more sophisticated, so do the methods employed by fraudsters. One of the most effective tools in a company's arsenal against identity fraud and duplicate accounts is advanced facial recognition, specifically 1:N Face Search. This technology allows businesses to compare a new user's face against an entire database of existing verified identities, identifying potential matches or blocklisted individuals. Didit, a leading identity verification company, offers a powerful 1:N Face Search solution, seamlessly integrated via its JavaScript (JS) SDK.

Understanding 1:N Face Search and Its Importance

1:N Face Search, also known as one-to-many face matching, is a biometric capability that takes a single facial image and searches for it across a large database of faces to find potential matches. Unlike 1:1 Face Match, which compares two specific faces to confirm identity, 1:N search is designed for discovery and fraud prevention. For businesses, this means:

• Duplicate Account Prevention: Detecting if a user is attempting to create multiple accounts under different credentials. This is crucial for platforms offering bonuses, limiting access, or maintaining fair usage policies.
• Blocklist Enforcement: Automatically identifying individuals who have previously been blocklisted due to fraudulent activity, policy violations, or other security concerns.
• Enhanced Security: Adding an extra layer of biometric security to ensure that only legitimate, unique users are accessing services.

Didit's 1:N Face Search is a critical component of a comprehensive fraud prevention strategy, providing high accuracy through advanced biometric algorithms and configurable thresholds to meet specific business needs.

Integrating Didit's Face Search with the JS SDK

Didit's developer-first approach shines through its well-documented APIs and intuitive SDKs, including the JavaScript SDK. Integrating 1:N Face Search into your web application is designed to be straightforward. The JS SDK allows you to capture a user's facial image (often during a liveness check) and then submit it for a 1:N search against your existing database of verified users.

The process typically involves:

1. Capturing the Image: Using the JS SDK to initiate a camera session and capture a high-quality facial image from the user. This is often part of Didit's Passive & Active Liveness detection to ensure the user is a real, present person.
2. Submitting for Search: Sending the captured image to Didit's API via the SDK for 1:N Face Search. The API performs the comparison against all previously approved identity verification sessions.
3. Receiving Results: The API returns a detailed report, including similarity percentages for potential matches, session IDs of matched users, and whether any match is on a blocklist. You can configure the similarity threshold (e.g., 70%) to fine-tune the strictness of the match.
4. Taking Action: Based on the results, your application can then decide whether to approve the user, flag them for manual review, or automatically decline the verification if a high-confidence blocklist match is found.

This seamless integration ensures that you can implement powerful biometric security without needing extensive backend development for facial recognition algorithms.

Configurable Thresholds and Warning Management

One of the key advantages of Didit's Face Search is its flexibility. Businesses can customize search sensitivity by setting different thresholds for similarity scores. A higher threshold (e.g., 90%) will yield fewer, but more confident, matches, suitable for high-security scenarios. A lower threshold (e.g., 70%) might return more potential matches, which could then be sent for human review. This allows organizations to tailor the system to their specific risk tolerance and operational requirements.

Furthermore, Didit provides comprehensive warning management. The system automatically handles conditions such as NO_FACE_DETECTED or MULTIPLE_FACES_DETECTED in the search image. Crucially, if a FACE_IN_BLOCKLIST is identified, the verification can be automatically declined, preventing known fraudsters from gaining access. The API response provides detailed warnings and their severity, allowing developers to build robust error handling and review workflows.

Real-World Applications and Benefits

The applications for Didit's 1:N Face Search are vast:

• Financial Services: Preventing fraudsters from opening multiple accounts to exploit financial incentives or engage in money laundering. Didit's AML Screening & Monitoring can further enhance compliance.
• Online Gaming/Betting: Ensuring fair play by preventing users from creating multiple accounts to claim bonuses or circumvent betting limits. Age Estimation can also be integrated for age-restricted content.
• E-commerce/Marketplaces: Detecting sellers who have been banned and attempt to rejoin under a new identity, protecting buyers and maintaining platform integrity.
• Telecommunications: Preventing SIM card fraud or multiple service subscriptions by a single individual, which can lead to significant losses.

By leveraging Didit's 1:N Face Search, businesses benefit from increased security, reduced fraud, improved compliance, and a more streamlined user verification process, ultimately saving time and resources.

How Didit Helps

Didit stands out as the AI-native, developer-first identity platform, making advanced identity verification accessible and powerful. Our 1:N Face Search, a core component of our 1:1 Face Match & Face Search capabilities, is designed for scalability and accuracy. We offer:

• Free Core KYC: Get started with essential identity verification features without upfront costs, making it easy to integrate and test our powerful tools.
• Modular Architecture: Didit's platform is built with composable identity primitives. You can easily plug-and-play Face Search alongside other solutions like ID Verification (OCR, MRZ, barcodes), Passive & Active Liveness, and Proof of Address to create a comprehensive verification workflow tailored to your needs.
• AI-Native Technology: Our solutions are powered by cutting-edge AI, ensuring high accuracy in biometric comparisons and rapid processing, even across large user databases.
• No Setup Fees: We believe in transparent pricing, allowing you to deploy advanced fraud prevention without hidden costs.
• Developer-First Experience: With an instant sandbox, public documentation, and clean APIs, integrating Didit's Face Search via the JS SDK is a smooth experience for developers.

Didit empowers businesses to automate trust and orchestrate risk with confidence, providing the tools needed to combat sophisticated fraud schemes and ensure a secure user base.

Ready to Get Started?

Ready to see Didit in action? Get a free demo today.

Start verifying identities for free with Didit's free tier.