How to Build a Mobile App with Face Recognition Technology?

The global facial recognition market is projected to grow to USD 24.28 billion by 2032, with a compound annual growth rate (CAGR) of around 15.50% during the forecast period. It is one of the fastest-growing biometric technologies, as the FRT market is experiencing tremendous growth.

While some view it as a threat to social rights, others see it as a promising innovation to ease several tasks. Regardless of which group you side with, none can deny that Facial Recognition Technology (FRT) has become increasingly valuable to many industries. And if you're an entrepreneur seeking the ideal industry to invest in, this is your perfect opportunity for recognition app development.

In today's digital tech world, Face recognition is used in retail apps, fleet management apps, security and surveillance apps, and many other software applications. If you are also planning to develop facial recognition software - this guide is for you.

In this guide, we will discuss everything about Facial Recognition Technology, what it is, the benefits it provides, and how it can be implemented to enhance the experience of your mobile app. So, let's get started.

Why is Facial Recognition Technology On the Rise?

Despite controversies, facial recognition startups are flush with VC cash - Techcrunch.

It is true to say that facial recognition is one of the most controversial and complex technologies to play with. But, still, the facial recognition startup sector is thriving and not suffering.

But, what is the reason behind the world going crazy on FRT? Entrepreneurs are finding new ideas every day to develop a solution based on FRT and investors are pouring money. Mobile apps like Jumio and AnyVision, announcing innovative rounds of financing every quarter, are proof.

If you are wondering WHY, we have the answer. Facial recognition technology has applications in every industry, and that’s why people all over the world want to try it in order to improve their business processes and develop new innovative solutions.

Additionally, FRT is undergoing significant evolution due to its increasing adoption across various sectors and industries. It empowers a myriad of apps with visual biometric identification abilities. As a result, facial recognition technology is being used for countless purposes, including:

• Biometric identity verification
• The automated checkout process in stores
• Campus security and workforce management
• Customer verification
• Patient identification
• Smart banking
• Improved public security
• Product discoverability by visual search
• Developing targeted marketing campaigns
• Diagnosing genetic disorders
• Store security and fraud prevention
• Running cardless ATM transactions
• Food Image Recognition
• Driving better user engagement on social media

Over the last few years, the use of facial recognition technology has increased significantly. In 2017, Apple released Face ID - the first-ever sign-in feature using FRT. Presently, more than 15-20% of the financial institutions in the USA including banks are using facial recognition to verify documents in order to validate users’ identities. As per a report from NIST, facial recognition systems and software were found 99% accurate when searching a database to match a picture. Bloomberg also believes that the global FRT market will reach USD 11.62 Billion by 2026, growing at a CAGR of 21%.

Seeing these statistics and Facial recognition market trends, it is clear how substantially FRT technology is expanding. If you are also planning to create a facial recognition software or use it to solve one of your business problems, let’s figure out how you can implement it.

Also Read: Top 5 Artificial Intelligence Tools for Mobile App Development

How to Implement Facial Recognition in Your App or Software?

Before investing in facial recognition app development, it is crucial to thoroughly understand the concept. What would you like to create and how should it work? Once you have it figured out, here are three ways to implement FRT in your mobile app:

1. Use Native Face Detection APIs

When you develop a mobile app, you create it for iOS, Android, or both platforms. These platforms offer their own APIs to help you integrate facial recognition features into the app. Even though the functionality these APIs provide will be limited, you can reduce the cost of face recognition app development by many folds using these options. You need to integrate the API into your app and ensure reliable picture detection and recognition features.

The most significant advantage of using native APIs is that they are optimized for a wide range of devices and can also benefit from hardware acceleration components. For example, Vision API is an Apple API for tasks related to computer vision. It has detectors that let you find faces, text, and barcodes.

Native Face Detection APIs: Pros & Cons

Aspect	Face Detection APIs Pros	Face Detection APIs Cons
Integration	Easy to integrate	Lack features
Cost	Affordable	Not many options are available
Performance	Optimized for most devices
Time & Effort	Requires no extra time & effort

2. Consider OpenCV Library

This is the commonly used library to develop facial recognition apps. It helps implement computer vision as well as ML in the applications. Essentially, OpenCV is an object detection platform that enables the detection of various objects and integrates well with multiple algorithms. This library works well for facial recognition and detection in many cases.

Initially, OpenCV was designed for standardizing the general interface of computer vision and for developing new models. Its main advantage is that it is free to use. However, its integration with mobile applications is somewhat complex, especially when it comes to face detection on Android. It requires extensive knowledge.

OpenCV (Haar-Cascade): Pros & Cons

Aspect	OpenCV (Haar-Cascade) Pros	OpenCV (Haar-Cascade) Cons
Cost	Available free of cost	Unable to detect side faces
Performance	Fast and capable of running in real-time (written in C/C++)	Performs poorly in different poses & lighting conditions
Model Size	Small model size (~400 KB and 10 MB for DLFD)	Hard to integrate with mobile apps
Technology	Utilizes modern deep learning algorithms	Exists as SDK only for this

3. Choose a Third-Party Solution

The market is full of various offers to make the development process smooth and highly convenient. You can use third-party services like Microsoft Face API, Google’s Cloud Vision API, Amazon Rekognition, Kairos, and many others to integrate facial recognition in your app or use FTR to improve your business processes. Usually, these services are paid but offer excellent functionality by recognizing not just faces but emotions, ethnicity, and many other things.

Third-Party Solution: Pros & Cons

Aspect	Third-Party Solution Pros	Third-Party Solution Cons
Capabilities	Advanced capabilities	High cost
Accuracy	High accuracy	Expertise needed
Support & Privacy	Privacy and support

Each third-party solution will have its own advantages and disadvantages. Therefore, you will need to choose carefully based on your business requirements and budget. Let’s now take a look at some of the best and most popular APIs to implement FRT in the next section.

4 Top APIs to Implement Facial Recognition in Your Mobile App Development

Mobile application APIs contribute directly to business growth. Therefore, if you plan to develop facial recognition software or a mobile app, it is advisable to utilize an API for implementation. APIs help businesses build innovative solutions while saving costs by increasing the productivity of developers.

Here are some of the best facial recognition APIs:

1. Microsoft Face API

Initially released in 2016, the Microsoft face API is a part of Microsoft's Cognitive Services. It is built on one of the most advanced facial recognition algorithms ever developed but it still has some limitations. The main features of the API are facial recognition, detection, and emotion recognition.

Using it, your mobile app can detect and identify similar faces while predicting the emotions associated with the face. For example, Uber - a top-notch ride-hailing application, also uses Microsoft's Cognitive Services to do a real-time ID check of their drivers. Users simply take a selfie in-app which is then used to find out the driver's identity by comparing it with the one in the database. Uber protects their driver's account by doing this check. However, the API has some limitations as well. It doesn't come as an SDK which limits developers' flexibility and also doesn't provide ethnicity detection.

2. Kairos Facial Recognition API

Based on deep learning algorithms, Kairos API provides quick and safe face detection. It is easy to understand, well-documented, and comes with a manual to help you create a good UX for your facial recognition application. Also, Kairos provides an SDK as well API for facial recognition. Their solution is able to detect faces, work with images as well as videos, and define gender, age, emotions, and ethnicity. As Kairos APIs include cloud-offloading of crucial data, the company offers privacy features and advanced security as well as audits for commercial use.

For integration, you can either use their cloud API or host it on your own servers for maximum control, privacy, and security. Here are some of the features of the Kairos platform:

• Face detection, identification, and verification
• Age and gender detection
• Facial coordinates matching
• Anti-spoof detection
• Diversity Recognition

3. AWS Rekognition API

Amazon Rekognition is based on highly scalable, proven, deep learning technology developed by Amazon's scientists to analyze billions of images and videos. You will not need expertise in machine learning to use this. It is an easy-to-use API that can analyze any picture or video stored in Amazon S3 quickly. Amazon continually updates its data, adding new labels and features to the service for improved facial recognition and comparison.

Amazon Rekognition provides highly accurate facial analysis, face search, and comparison capabilities. You can detect, compare, and analyze faces for several use cases, including cataloging, user verification, public safety, and counting. Some of the common use cases of Amazon Rekognition API are:

• Searchable image and video libraries
• Sentiment and demographic analysis
• Detection of personal protective equipment
• Facial search and unsafe content detection
• Celebrity recognition and text detection

4. Google Cloud Vision API for Face Detection

Being a part of Google's cloud platform, the Google Cloud Vision API is a fast and reliable solution. But, as of now, it is just available for face detection and not for facial recognition. Cloud Vision offers pre-trained models via the API and the ability to create customized models using the AutoML vision application. The API lets users understand and analyze the content of an image by integrating ML models in an easy-to-use API. It can help you classify images into several categories, identify text in an image, and detect objects and faces as well. Here is an overview of its features:

• Object and face detection
• Sentiment detection (faces)
• OCR
• Nudity/Violence Detection
• Logo/Landmark Detection

Face Recognition App Development: Step-by-Step Guide!

Here we've thoroughly discussed the step-by-step face recognition app development process in detail and how we help you to build one:

1. Define Your App’s Purpose

Before starting development, clearly define the purpose of your face recognition app. Identify whether it’s for security, attendance tracking, healthcare, or user authentication, and understand your target audience to ensure the app meets real user needs.

2. Choose the Right Face Recognition Technology

Select the most suitable face detection technology for your app. Options include Native APIs for easy integration, OpenCV (Haar-Cascade) for lightweight solutions, or third-party solutions that offer advanced capabilities and high accuracy.

3. Collect & Integrate Data

Integrate data from multiple sources, including wearable devices, EMRs/EHRs, and images, to build a comprehensive dataset. This ensures the AI model has enough information for precise face recognition while maintaining user privacy.

4. Design UI/UX

Create a user-friendly interface that guides users through face scanning effortlessly. Focus on mobile optimization, accessibility, and intuitive navigation to ensure smooth interaction with the app.

5. Develop the Backend

Build a robust backend that supports real-time face detection and recognition. Implement secure databases for storing user profiles and ensure fast processing speeds for accurate recognition results.

6. Implement AI & Machine Learning

Use deep learning algorithms to train your face recognition system. Include diverse datasets to handle different poses, lighting conditions, and expressions, improving overall accuracy and reliability.

7. Testing & Quality Assurance

Conduct extensive testing across multiple mobile devices and environments. Focus on accuracy, speed, and false positives/negatives, and gather feedback from beta users to refine the app.

8. Security & Privacy Measures

Ensure all user data is encrypted and protected. Include user consent, data anonymization, and regular security updates to comply with privacy regulations like HIPAA or GDPR.

9. Deployment

Launch your app on the iOS App Store and the Google Play Store. Set up backend servers for real-time recognition and ensure the app functions smoothly on all targeted devices.

10. Maintenance & Updates

Continuously monitor app performance, update AI models, and add new features based on user feedback and analytics. Regular updates ensure your face recognition app remains accurate, secure, and competitive.

How Much Does it Cost to Build a Facial Recognition Mobile App?

The cost to build a facial recognition app can vary widely depending on its complexity, features, and the development team’s location. For a basic app with essential face detection features, the cost usually ranges between $8,000 and $25,000. If you want an intermediate app with real-time recognition, API integrations, and some AI capabilities, the price can go up to $40,000–$120,000. For an advanced enterprise-level app with comprehensive AI models, high scalability, anti-spoofing, and enhanced security, the development cost can exceed $120,000–$200,000.

Several factors influence the final cost: the number and complexity of features, whether it’s built for iOS, Android, or both, the hourly rates of developers (which vary by region), and ongoing maintenance for server hosting, AI model updates, and compliance with privacy regulations. Additionally, cloud-based APIs like AWS Rekognition or Microsoft Azure can incur small per-image charges, while hardware requirements like high-quality cameras or servers may add to the initial investment.

Bonus Read: iOS App Development Cost: Full Guide

Build Your Facial Recognition App Development With Apptunix Experts

Facial recognition technology, or FRT, is gaining popularity with every passing day. It is making people's lives easier with its applications in various industries. However, using this technology is not straightforward due to the challenges it presents. You can for sure expedite the process of facial recognition app development with the help of tools, platforms, APIs, SDKs, and frameworks, but to train the AI-based solution to work the way you want, you will require experienced experts who have worked on such projects.

At Apptunix, the #1 mobile app development company based in the UAE, USA, & India, we have expertise in developing such applications and software. Also, we are eager to help you in your venture. Our engagement models are highly reliable and flexible, and that's just one advantage of working with us. We can help you build a powerful facial recognition application that entirely fits your specific business needs. While working with us, you also get:

• A non-disclosure agreement to protect your idea's uniqueness
• 100% protection of your confidential data
• 100% transparency with no hidden costs
• Pre-vetted software developers, AI and ML experts
• A team of QA engineers to ensure your app's quality.

Collaborating with a leading software company like Apptunix is key to your project's success. And, our work doesn't stop once you launch your application, we believe in long-term partnerships and stand by our clients while providing post-launch support and upgradation services. Get in touch now and start your journey to success.