Face Recognition for Mobile Apps: Fast SDK & API Integration Guide

Face recognition is changing the mobile app industry by offering a secure and convenient way to interact with devices.

Users can unlock smartphones, authorize financial transactions, and experience improved app usability more easily than with traditional methods like passwords and PINs.

The global face recognition market, valued at $6.3 billion in 2023, is projected to grow to $13.4 billion by 2028, reflecting an annual growth rate of 16.3%.

This trend is evident in mobile apps, where 72% of users prefer face biometrics over passwords, contributing to the mobile biometrics market's anticipated expansion to $184.8 billion by 2031.

Face recognition technology is driven by four core steps: face detection, face alignment, template extraction, and face matching. Let's explore how they work together.

Face Detection
Face detection finds and marks human faces in images or videos by drawing bounding boxes around them. Modern face recognition systems use AI and machine learning (like Convolutional Neural Networks) to detect faces even in tough conditions such as bad lighting or different angles.

Face Alignment
After detection, the face is adjusted to a standard frontal position by using key facial landmarks (eyes, nose, mouth) to correct scale, rotation, and perspective differences. This makes the face look consistent for better accuracy in later steps.

Biometric Template Extraction
Template extraction converts the aligned face into a mathematical model that captures its unique features, such as shapes, textures, and patterns.
Modern algorithms create a numeric vector that represents the face, allowing easy comparison with other templates.

Face Matching (1:1 and 1:N)

• 1:1 Face Matching (Face verification) compares the face template with one reference template to confirm identity, like unlocking a phone.

• 1:N Face Matching (Face identification) compares the face template with many others in a database to identify someone, often used in security and surveillance to find people in a crowd.

Face recognition improves security in mobile apps with seamless identity verification and authentication. Here are some examples:

• Unlocking Devices: Use face recognition software to unlock smartphones and tablets quickly and securely, instead of using passwords or PINs.

• Authorizing Financial Transactions: Add an extra security step by requiring face verification before completing payments or transfers.

• Enhanced Login Security: Add a layer of security as a secondary authentication factor along with passwords, creating a multi-layered defense.

• KYC (Know Your Customer) Compliance: Face recognition technology has proven to be highly effective in KYC processes, verifying the identity of users during registration or onboarding. This helps companies meet regulatory requirements, prevent fraud, and ensure that users are who they claim to be.

Face recognition software in mobile apps offers significant convenience for users:

You don't need to remember a complex password or update it every time there's a security breach at some service provider.

If biometrics is used as a second authentication factor, the process becomes even more streamlined:

• You don't have to open an app to retrieve a one-time code, like with Microsoft Authenticator.

• There's no waiting for an SMS code or manually typing it in.

Native Face Recognition API
Face recognition APIs provided directly by the device’s operating system (OS), such as iOS or Android. These APIs are built into the operating system, so developers can add face recognition to their apps.

Examples:

• Apple’s Face ID (part of iOS)
• Android’s Face Authentication API (part of the BiometricPrompt framework)

Open-Source Face Recognition SDK / API
Publicly available software components for face recognition, with source code that can be freely viewed, modified, and shared.

Examples:

• 3DiVi Open-Source Face SDK
• InsightFace
• Dlib

Commercial Face Recognition SDK / API
Face recognition SDKs/APIs from third-party companies, available as paid products with licensing agreements.

Examples:

• 3DiVi Face SDK
• 3DiVi Omni Platform
• Microsoft Azure Face API
• Amazon Rekognition

Technical Requirements
Make sure the face recognition technology you choose works with your app's programming languages, hardware, and operating systems. Here’s how to approach it:

Programming Languages Compatibility
Choose an SDK / API with Java, Kotlin, Flutter, or Swift support or wrappers.

Hardware Specifications

• Cameras: Check that the face recognition technology works with your camera hardware. Higher resolution cameras improve accuracy.
• Processing Power: Ensure that the device can handle real-time face recognition.

Operating System Compatibility

• iOS: Confirm support for the iOS versions you target.
• Android: Check compatibility with various Android versions and devices.
• Cross-Platform: Choose a technology that works on both iOS and Android.

Integration Security
Here, we’ll outline a couple of fundamental considerations to keep in mind for a secure deployment:

For the Server (when using an API):

• Data at Rest: Encrypt facial data stored on devices or servers using strong encryption algorithms such as AES (Advanced Encryption Standard).
• Data in Transit: Use secure communication protocols (e.g., HTTPS) to encrypt data transmitted between the app and server.

For Smartphones:

• Lock the App to the Front Camera: This makes it more difficult for attackers to launch spoofing attacks using alternative devices.
• Implement Photo Quality Control: By adding quality checks for facial images captured, you will reduce the likelihood of face recognition errors.

1. Define your goal, technical requirements, and use case (face authentication, user identification, emotion detection, etc).
2. Choose the right solution: Pick between an SDK or API and decide if you want a native, open-source, or commercial option.
3. Integrate face recognition features into your app.

4.Test and handle errors.

• Test under different conditions (poor lighting, various angles, obstructions).
• Manage errors (e.g., no face detected, multiple faces detected, recognition failure, etc.) with clear prompts, guiding users on what to do (e.g., “Position your face within the frame”).

3DiVi offers solutions that streamline the development process and ensure successful integration into mobile apps. Here's how we can help:

Face recognition SDK and API
Our tools work with C++, Java, Kotlin, Swift, and Flutter on both Android and iOS, offering high accuracy and performance, ensuring easy integration, top-rated accuracy in NIST tests, and high performance.

Expertise and Support
Our team provides help from setup to ongoing maintenance, making sure your app runs smoothly.

Customizable Solutions
Whether you need a simple setup or a custom solution, 3DiVi offers flexible options to fit your needs.

Ready to integrate face recognition technology into your app? Reach out to discuss your project and see how our expertise and solutions can turn your vision into reality.