How To Make Face Recognition Software
Latest posts by Aran Davies (see all)
- Dev Team Roles and Responsibilities - 5 Jul, 2022
- Microservices Architecture vs Monolithic Architecture - 5 Jul, 2022
- How to Transition Away from a Bad Developer? - 29 Jun, 2022
In this guide I will explain how to make face recognition software.
The steps to make face recognition software are 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 important features for facial recognition systems, 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 computer vision. 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.:
- Read “AI development life cycle: explained” to understand the lifecycle of an AI development project.
- Consult “How much does it cost to develop an AI solution for your company?” to understand how to estimate such a project, by taking into account the manpower, infrastructure, tools, and other costs.
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;
- 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.
Hire expert developers for your next project
1,200 top developers
us since 2016
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.
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:
- “User interface design guidelines: 10 rules of thumb”, for designing the web app UI;
- “Design | Create intuitive and beautiful products with material design”, for designing the Android app UI;
- “Human Interface Guidelines”, for the iOS app UI design.
11. Developing the web app
Code the web app using Node.js, the performant and scalable open-source runtime environment. This involves the following:
- Integrate the Amazon Rekognition and Twilio APIs.
- Read “Adding a database to your Elastic Beanstalk environment” to learn how to integrate database resources on AWS Elastic Beanstalk.
- Test and deploy the web app on AWS Elastic Beanstalk. You can read “Deploying Node.js applications to AWS Elastic Beanstalk” for guidance.
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.
13. iOS app development
I recommend that you code the iOS app using Swift, using Xcode, the popular IDE for developing apps for Apple’s platforms. Integrate the Amazon Rekognition and Twilio APIs.
Planning to launch a facial recognition software for your organization?
You can certainly expedite the project of facial recognition technology with the help of platforms, tools, frameworks, and guidelines, however, developing the best facial recognition software 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.
DevTeam.Space can help you with creating market-competitive facial recognition solutions. You can get in touch via this quick form explaining your initial requirements for a facial recognition feature. One of our account managers will reach out to you to discuss in detail facial recognition process for your project and link you with experienced software developers.
Frequently Asked Questions
1. How to make face recognition software?
Important steps for making face recognition systems include investing in cloud infrastructure for solution development and partnering with software developers experienced in face recognition machine learning algorithms and face recognition technology.
Depending on the size of the project, building facial recognition software can cost between 10,000 US dollars to 30,000 US dollars.
You can use face recognition datasets created by fellow researchers for your facial recognition system or use libraries and tools such as Open CV and Anaconda to develop your own custom face recognition database. Follow this tutorial for more on this.