Build Face Search (1:N) in Flutter with Didit's SDK

Seamless Flutter IntegrationDidit's Flutter SDK provides a straightforward way to add advanced 1:N Face Search functionality, including biometric matching and blocklisting, directly into your mobile applications.

Robust Fraud PreventionUtilize Face Search to automatically detect and prevent duplicate accounts and identify individuals on watchlists, significantly enhancing your platform's security posture.

Configurable Security SettingsCustomize similarity thresholds and handle multiple face detections to fine-tune the balance between security and user experience according to your specific application needs.

AI-Native and Developer-FriendlyDidit offers an AI-native, modular identity platform with clean APIs, a free core KYC tier, and no setup fees, making advanced biometric integration accessible for all developers.

The Power of 1:N Face Search in Modern Applications

In today's digital landscape, verifying user identities and preventing fraud are paramount. Traditional 1:1 face matching, where a user's live selfie is compared to their ID document, is a crucial first step. However, for comprehensive security, businesses need to go further. This is where 1:N Face Search comes into play. Instead of just comparing two images, 1:N Face Search allows you to take a new user's biometric data and search it against a vast database of previously verified users or known fraudsters. This capability is invaluable for detecting duplicate accounts, preventing repeat fraud attempts, and maintaining a high level of integrity across your user base. Imagine a scenario where a fraudster attempts to sign up with slightly altered details but the same face—1:N Face Search can instantly flag this.

Didit's 1:N Face Search feature is designed to address these challenges head-on. It searches for a specific face across all your approved identity verification sessions, providing powerful insights into potential risks. This capability is not just about security; it's about building a trusted ecosystem for your users and your business.

Integrating Didit's Face Search with the Flutter SDK

Integrating advanced biometric features into mobile applications can often be complex and time-consuming. Didit simplifies this with its developer-first approach and comprehensive SDKs, including a robust Flutter SDK. This allows developers to build sophisticated identity verification workflows directly into their iOS and Android apps with a single codebase.

To begin, you'll need to set up the Didit Flutter SDK in your project. This typically involves adding the SDK to your pubspec.yaml file and configuring native dependencies for both iOS and Android. Once installed, your application can initiate a verification session, capture the necessary biometric data (such as a selfie or liveness check), and then leverage Didit's backend to perform the 1:N Face Search. The process involves your backend creating a session with Didit and passing the session token to your Flutter app. The app then uses this token to interact with Didit's services, capturing the user's face and sending it for processing.

The beauty of Didit's modular architecture is that Face Search can be seamlessly integrated into existing workflows. Whether you're conducting initial ID Verification, performing Passive & Active Liveness checks, or using 1:1 Face Match, the 1:N Face Search can run in the background or as an explicit step to cross-reference against your entire user base.

Understanding Face Search Results and Warnings

After initiating a Face Search, Didit provides a comprehensive report detailing any matches found. The Face Search Report includes crucial information such as total_matches, a list of matches with session_id, similarity_percentage, and even partially masked user_details from the original verification session. This level of detail allows businesses to make informed decisions about user identities and potential fraud. For instance, a high similarity percentage to a previously blocklisted user would immediately flag a high-risk scenario.

Didit also provides a robust warning system to guide your decision-making. Warnings such as NO_FACE_DETECTED or MULTIPLE_FACES_DETECTED ensure the quality of the input image. Critically, the FACE_IN_BLOCKLIST warning automatically identifies if the detected face matches an entry in your configured face blocklist, providing an immediate automatic decline condition. You can configure various settings, such as the Similarity Threshold (e.g., 70% minimum) to control the sensitivity of matches and whether to Allow Multiple Faces in a search image, giving you granular control over your security policies. This configurability allows you to tailor the system to your specific risk appetite and operational needs, ensuring that you catch genuine threats while minimizing false positives.

Practical Applications of Face Search for Fraud Prevention

The applications for 1:N Face Search are extensive, particularly in fraud prevention and compliance. For financial services, it's a critical tool for detecting duplicate loan applications or identifying individuals attempting to open multiple accounts. E-commerce platforms can use it to prevent bonus abuse or identify account takeover attempts by cross-referencing new login attempts against known user biometrics. In online gaming or social platforms, it helps maintain fair play and prevent the creation of bot networks or fake profiles.

Beyond fraud, Face Search can also enhance user experience by streamlining re-verification processes. Instead of re-entering credentials, returning users could potentially be identified through a quick face scan, provided their biometric data matches a previously verified identity within your system. This balances security with convenience, a hallmark of Didit's design philosophy. By leveraging Didit's AI-native capabilities, businesses can automate trust and reduce reliance on slow, error-prone manual reviews.

How Didit Helps

Didit provides an unparalleled solution for implementing 1:N Face Search and other advanced identity verification features. Our platform is built on a modular architecture, allowing you to plug-and-play identity checks like ID Verification, Passive & Active Liveness, and 1:1 Face Match, all integrated seamlessly with Face Search. Didit's AI-native approach ensures high accuracy and efficiency in biometric comparisons and fraud detection.

With Didit, you benefit from a developer-first experience, offering an instant sandbox, comprehensive public documentation, and clean APIs for quick integration. We eliminate the barriers to advanced identity verification by offering Free Core KYC and a pay-per-successful check model, with absolutely no setup fees. Our Face Search product, combined with features like AML Screening & Monitoring and Phone & Email Verification, provides a holistic approach to identity and fraud management, ensuring you have the tools to protect your business and users effectively.

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.