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How To Build Facial Recognition Software

We at DevTeam.Space recently developed a face, sex, age, video emotion recognition, and movement tracking system for NEC, the Japanese IT giant. During the project we gained a clear picture of how businesses can benefit from implementing facial recognition software.

It is likely that your business or organization could take advantage of this exciting new technology as well.

If your company is looking to develop such a solution then you will need to learn how to build facial recognition software from scratch. This is exactly what we will explain in this guide.

Contents

What is facial recognition software?
A brief overview of how facial recognition software works
The advantages of facial recognition technology
Key facial recognition use cases
The market for facial recognition software
A few examples of popular facial recognition software
The features desired in a facial recognition software
Building facial recognition software
Planning to launch a facial recognition software for your organization?

What is facial recognition software?

A few basics before we start! Facial recognition software belongs to the category of biometric software. This is software that maps people’s facial features. Such software uses mathematical mapping techniques, and stores this data as “faceprints”.

Subsequently, facial recognition software uses deep learning capabilities to compare a digital image with the faceprints that were stored earlier. This way, such software can be used to allow identity verification. You can read more about this in “facial recognition”.

A brief overview of how facial recognition software works

There are various flavors of facial recognition technology, however, they generally work as follows:

  1. It captures your image from a picture or a video.
  2. The software reads the geometry of your face, e.g., the distance between eyes, the distance from forehead to chin, etc.
  3. It also identifies facial landmarks, and it stores all these data as your facial signature.
  4. A database of facial signatures might have many such records, and the software will use deep learning algorithms to find a match in this database.
  5. When it finds a match, the software then uses this information to perform further activities, e.g., authenticate you to use a computer system.

Read more about this in “How does facial recognition work?”.

The advantages of facial recognition technology

Facial recognition technology offers many advantages, e.g.:

  • Security: Law enforcement agencies can use this technology to track down criminals.
  • Faster identity verification: Facial recognition technologies work at a fast pace, therefore, businesses/organizations looking for quick identity verification can take advantage of it.
  • Ease of integration: You can easily integrate facial recognition solutions with your other software.

There are also privacy concerns around facial recognition, and you can read “The threats and benefits of facial recognition: what should we know?” to learn more.

Key facial recognition use cases

There are several prominent use cases for facial recognition technology, e.g.:

  • Biometric authentication-based security: Biometrics systems use this technology to aid authentication and access control systems.
  • Automated image recognition: Facebook uses facial recognition to identify the users of this social networking site.
  • Security: Security and law enforcement agencies use facial recognition to identify a large number of people and for less intrusive monitoring.
  • Additional functionalities for devices: Smartphone makers like Apple use facial recognition for biometric identification.

Read more about these use cases in “Facial recognition: advantages and disadvantages”.

The market for facial recognition software

The benefits of facial recognition software seem to outweigh the privacy concerns surrounding it. A recent survey shows that 54.3% of Americans believe that airports should be able to use such software. 54.8% of Americans believe that the technology should be used unfettered if it enhances security, and you can read about it in “Majority of Americans support facial recognition”.

The global market for facial recognition software will likely grow from $3.2 billion in 2019 to $7 billion by 2024, accompanied by a CAGR of 16.6%. Read more about this projection in this MarketsandMarkets report.

Following are the key players in this market:

  • NEC;
  • Aware;
  • Gemalto;
  • Ayonix Face Technologies;
  • Cognitec Systems GmbH;
  • NVISO SA.

A few examples of popular facial recognition software

Let’s review the following examples of market-leading facial recognition software:

DeepVision

Founded in 2013, DeepVision provides computer vision software for use in cars, robots, drones, and other relevant machines. Its facial recognition technology is powered by deep learning. It can detect and recognize faces, moreover, it can estimate age and gender.

FaceFirst

FaceFirst was founded in 2007, and it offers face recognition security software. Retailers can use this software to prevent shoplifting, moreover, security agencies can use it in the airports. It’s useful for law enforcement agencies too.

The company claims that its solution is scalable, performant, and camera-agnostic. Its software provides an integration platform, moreover, it offers real-time alerts using SMS, email, and push notifications.

The features desired in a facial recognition software

An effective facial recognition software needs the following features:

  • A robust database: Facial recognition software needs robust databases of persons of interest so that they can later match faceprints against it.
  • Powerful matching algorithms: Matching algorithms should use a large number of points on a face to match it against the database, the more the better!
  • Scalability: Great facial recognition software should scale effortlessly across a large number of locations.
  • Privacy protection: People increasingly realize that their face is now important data. To address privacy concerns around facial recognition technology, businesses should use robust privacy protection features, e.g., data-encryption and precautions against data breaches.
  • Regular purging of surveillance data is important, so is the ability to prevent profiling people by race, age, gender, etc.
  • Analytics: A robust facial recognition software needs predictive analytics capabilities that can keep up with the demand.

Read more about these features in “5 features that every enterprise face recognition solution must have”.

Building facial recognition software

I will now explain the steps to develop a facial recognition software, which is as follows:

1. Define the project scope

I recommend that you initially induct a project manager (PM), an IT architect, and business analysts, and define the project scope. You should plan to launch the proposed facial recognition software on the web, Android, and iOS.

Include the features I have described above, e.g., database, matching algorithms, privacy, analytics, etc., moreover, pay close attention to scalability.

2. Agree on a project methodology

You need an IT architect to join the PM now, and together they should choose the right methodology for this project. Using the Agile methodology makes sense since you can deploy the facial recognition solution in manageable sprints.

Facial recognition software uses Artificial Intelligence (AI) capabilities like deep learning. Agile is suitable for such projects, and you can read about it in “5 ways to improve AI/ML deployments”.

3. Formulate a development approach

The PM and architect should work together and define a development approach, and I recommend the following:

  • Use a managed cloud services provider so that you don’t need to manage the IT infrastructure.
  • Utilize facial recognition software development tools to expedite the development.
  • Enhance the test coverage with test automation aids.

I have explained the value of such an approach in “What is the best development approach to guarantee the success of your app?”.

4. Estimate and plan the project

The PM and architect now need to plan the project including detailed cost estimation. We have useful guidelines that can help, e.g.:

5. Form the complete project team

You now need to form the complete project team, therefore, you need to induct the following roles:

  • AI developers with deep learning skills;
  • UI designers;
  • Web developers with Node.js skills;
  • Android developers with Java skills;
  • iOS developers with experience in Swift;
  • Testers;
  • DevOps engineers.

I recommend that you should induct a field expert development team since this will likely be a complex project. Read “Freelance app development team vs. field expert software development teams” to learn more about this.

6. Sign-up for a managed cloud service

Since you will launch your facial recognition app on the web, Android, and iOS, I recommend that you sign-up for a reputed managed cloud service. I recommend that you use AWS Elastic Beanstalk for developing the web app since you can get the following advantages:

  • Elastic Beanstalk is the Platform-as-a-Service (PaaS) platform from AWS, and it manages the cloud infrastructure, networking, storage, operating system, middleware, and runtime environment. You can focus on development.
  • It’s easy to integrate database resources, 3rd party APIs, and DevOps services when you use Elastic Beanstalk.
  • You can easily scale your web app when using Elastic Beanstalk, thanks to its application performance monitoring (APM) and auto-scaling solutions.

You should use AWS Amplify, which is the Mobile-Backend-as-a-Service (MBaaS) platform from AWS, for developing the mobile app. Amplify offers several advantages, e.g.:

  • You can focus on the front-end since Amplify manages the cloud infrastructure, persistent storage, etc. This eliminates the need for you to develop and manage the mobile backend.
  • Developers can easily integrate 3rd party APIs when using Amplify, moreover, it’s easy to implement features like user management, security, and push notifications.
  • Scaling a mobile app is easier when you use Amplify.

7. Get a development tool for facial recognition software development

You can expedite the project with the help of a development tool, therefore, I recommend that you use Amazon Rekognition, a reputed API solution for image and video recognition. It offers the following features and advantages:

  • Your app can identify objects, people, text, scenes, and activities with Amazon Rekognition.
  • This API provides highly accurate facial recognition and analysis of images and videos.
  • It uses a reliable and scalable deep learning suite of software.
  • The API is easy to use, and your team can read “Getting started with Amazon Rekognition” to learn how to use it.
  • Amazon Rekognition offers simple integration, and the system learns with new data.
  • It’s a fully managed service that offers batch and real-time analysis.
  • The API has robust security features.

Facial recognition is a key use case of Amazon Rekognition. Check out the Amazon Rekognition pricing plans.

8. Sign-up for a bulk-SMS solution

The mobile app needs the push notifications feature, therefore, I recommend that you use the Twilio bulk SMS solution for this. Twilio offers its Programmable SMS solution, and you can consult the following resources to use it:

Check out the Twilio pricing plans.

9. Find a test automation aid to improve your test coverage

The web app should work with a wide range of browsers, moreover, the mobile apps need to work with all common mobile devices. You need a test automation aid to achieve this, and pCloudy offers over 5,000 device-browser combinations on the cloud.

Read the pCloudy documentation overview to learn about this solution. You can check out the pCloudy pricing packages to understand its different pricing plans.

10. Design the user interface (UI)

The UI design team needs to design user-friendly interfaces for the web and mobile apps, therefore, I recommend the following resources:

11. Developing the web app

Code the web app using Node.js, the performant and scalable open-source runtime environment. This involves the following:

12. Developing the Android app

I recommend that you code the Android app using Java, and you should use Android Studio, the popular IDE for Android development. You need to integrate the Amazon Rekognition and Twilio APIs in the app.

Test the app using Espresso, and the pCloudy device lab. Read “Publish your app” to learn how to publish the app to Google Play.

13. iOS app development

I recommend that you code the iOS app using Objective-C, using Xcode, the popular IDE for developing apps for Apple’s platforms. Integrate the Amazon Rekognition and Twilio APIs.

Test the app using XCTest and the pCloudy device lab. You can read “Submitting iOS apps to the App Store” to find instructions on how to publish the app to the Apple App Store.

Planning to launch a facial recognition software for your organization?

You can certainly expedite the project with the help of platforms, tools, frameworks, and guidelines, however, developing a facial recognition app can be a complex project. I recommend that you engage a reputed software development company for such projects, and read our guide “How to find the best software development company?” to find one.