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How to build an identification app is perhaps a key question if you are targeting this space.
Organizations and individuals continue to look for user-friendly and simple solutions. Thanks to technology solutions, you can meet their requirements better. Building an identification app can be complex, however, this guide can help. Read on.
Why creating face recognition apps can help: Their growing market
Various sectors and functions need face detection solutions. The following are a few examples:
- Homeland security;
- Criminal investigation involving identifier systems;
- Intelligent signage;
- Identity management;
- Physical security;
- Photo indexing and sorting.
Several factors drive the growth of face recognition technology solutions. A few examples are as follows:
- The increased focus on face detection in national security, physical security, mobile security, surveillance, etc.;
- The growing availability of cloud computing services;
- Advancements in “Artificial Intelligence” (AI) and its various capabilities like “Machine Learning” (ML), “Computer Vision”, “Image Recognition”, etc.
- The growing sophistication and reach of popular mobile operating systems like Android, iOS, etc.;
- The availability of “Application Programming Interfaces” (APIs), “Software Development Kits” (SDKs), etc.
The market for face recognition apps is growing. A MarketsandMarkets report estimates that this market will grow from $3.8 billion in 2020 to $8.5 billion in 2025. The report projects an impressive CAGR of 17.2% during the 2021-2025 period.
You can make a mark in the growing market by offering a robust face recognition app.
Build an identification app using face recognition technology
Take the following steps to create a facial recognition app:
1. Hire an expert team to kick-start the project
Hire remote developers and designers to build a team with enough expertise to make a great beginning to your project. You need an experienced project manager (PM), a software architect, and a team of business analysts in this initial team.
Look for a PM with solid project management skills, competencies, and experience. You should look for experience in leading similar projects. You need business analysts with the skills and experience to elicit business requirements from end-users.
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You need an architect with deep knowledge in the space of face recognition software. The architect needs sound knowledge of the following high-priority technologies:
- Image recognition: The AI capability for image processing and extracting useful information from images;
- Signal processing: The technology to extract information from signals like sounds, biological measurements, etc.;
- Computer vision: The AI discipline to build systems that gather information from images, videos, etc.;
- Machine Learning (ML): Machine learning algorithms will help the target computer system to make decisions based on data.
The architect also needs expertise in software engineering, architecture, software project planning, software development methodologies, cloud computing, etc.
2. Define the project scope
You need to define the project scope, and you need to decide on the functionality to offer. Gather business requirements by talking to business stakeholders. Conduct detailed interviews for this. BAs need to elicit functional requirements and document them.
This involves several aspects, e.g.:
- Choosing the target platforms like web, Android, iPhone, etc.;
- Choosing the data sources, e.g., search engines, social networks, etc.;
- Deciding on the face recognition features;
- Identifying the parameters to analyze, e.g., facial features of the human face, hairstyle, voice recognition, etc.;
- Deciding on capabilities to offer, e.g., visual search, authentication, biometrics, etc.
You might launch a “Minimum Viable Product” (MVP) first. Prioritize features for the MVP, and use our MVP development guide for this.
The architect needs to help in defining the non-functional requirements (NFRs). The following are a few examples of NFRs:
Are you developing facial recognition software for a heavily-regulated industry like healthcare? NFRs like security become very important in that case. Pay sufficient attention to capture NFRs adequately.
3. Choose an appropriate software development methodology
Choose the right software development methodology for your proposed image recognition software app development project. The choice depends on your project and business context.
For example, do you have the business requirements finalized? You can use the waterfall methodology for such projects. You will have sequential phases for such projects, e.g., requirements definition, design, development, testing, deployment, maintenance, etc.
On the other hand, do you expect the requirements to change frequently? Iterative development might suit such projects with fluid requirements better.
Consider the “Agile” software development methodology for such projects. Furthermore, this methodology suits you well when you develop web and mobile applications. We have explained this in our Waterfall vs Agile comparison.
4. Identify the AI capabilities and data sources for app development
You will likely need more than just facial recognition technology in your project. The exact range of AI capabilities required will depend on your project requirements. For example, you might need the following:
- Machine learning algorithms;
- Deep learning, including “Artificial Neural Networks” (ANNs);
- Flexible Image recognition algorithms;
- Voice recognition capabilities.
Analyze these carefully. Implementing a systematic review process at this stage is important. Such a review might prompt you to analyze at a more granular level. E.g., you might find that you need to use face descriptors.
You need to choose the right data sources to “train” your face recognition system. Look for appropriately varied data sources, and find data sets of appropriate volume. Refer to our guide to the AI development lifecycle for help.
5. Decide the approach to develop the web and mobile app using the image recognition technology
Formulate a software development approach. Focus on the following aspects:
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- Securing the web app, mobile app, and face recognition software;
- Choosing between native mobile app development vs using frameworks like React Native;
- Choosing the appropriate IT infrastructure options like cloud computing;
- Deciding on using the public, private, or hybrid model in case you choose to use a cloud computing platform;
- Choosing between 3rd-party APIs vs developing own APIs;
- Identifying the right technology stack;
- Choosing the right development, testing, and DevOps tools;
- Implementing the right quality management processes, methods, and tools (PM&T).
Our guide to choosing the right software development approach can help.
6. Plan the image recognition app development project
Planning will help you to succeed in this project. Include the following in the planning exercise:
- Identifying the various tasks in the different phases;
- Estimating tasks and phases;
- Determining dependencies;
- Scheduling tasks and phases;
- Planning the hiring process;
- Formulating approaches for risk management, issues management, communications management, etc.
Look for the right project management tools.
7. Build the right application security solution
AI software systems process a great deal of sensitive information. An identification app utilizing facial recognition technology will do that too. Build application security solutions, which should focus on the following:
- Mitigating key application security risks highlighted by the “Open Web Application Security Project” (OWASP) top ten reports;
- Using antivirus, firewalls, real-time threat intelligence, etc.;
- Utilizing multi-factor authentication (MFA);
- Using encryption, digital signature, etc.;
- Securing APIs.
Implementing security and compliance testing in your CI/CD pipeline helps, as we have explained in our guide to secure FinTech apps.
8. Choose suitable cloud computing platforms
For a start-up company, cloud computing platforms eliminate upfront investments in IT infrastructure. Enterprises are also adopting cloud computing to reduce costs.
Cloud computing platforms help you to access computing resources on-demand. Depending on the kind of platform, a cloud provider might provide much more too.
Take the example of a “Platform-as-a-Service” (PaaS) platform. PaaS platforms like AWS Elastic Beanstalk and Microsoft Azure PaaS are cloud platforms.
They manage the infrastructure, network, operating system, middleware, and runtime environment. You focus on application development and data, which expedites your project.
Cloud platforms can help you with mobile app development too. Developing and managing a mobile backend can take plenty of time and effort.
“Mobile-Backend-as-a-Service” (MBaaS) platforms manage the infrastructure, persistent storage, etc., which reduces your backend development effort. MBaaS platforms like AWS Amplify from Amazon Web Services (AWS) can expedite your project.
Cloud platforms can help you to incorporate image recognition and other image recognition APIs. They can also help you to scale your web and mobile apps. Analyze your project requirements to determine the kind of cloud platforms you need.
9. Evaluate leading 3rd-party APIs like the Luxand Face Recognition API
You might choose to use leading 3rd-party APIs for face recognition. Evaluate different APIs like the Luxand Face Recognition API. You can also use the Luxand FaceSDK.
Luxand offers the following advantages:
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- FaceSDK supports Microsoft Visual C++, C#, Objective-C, Swift, Java, Python, etc. You can build an Android app, furthermore, you can build apps for various Apple platforms. This SDK helps you to create web apps. You can also use desktop apps for Windows, Mac, Linux, etc.
- Your app will be able to detect faces on still images and real-time video streams.
- The SDK supports detecting facial features.
- You can use it for several key use cases like identification, surveillance, etc.
- Luxand offers comprehensive documentation for its API and SDK.
Amazon Rekognition is another option. It enables you to add image and video analysis to your app. You get the following advantages:
- You can use Amazon Rekognition without deep learning or machine learning algorithm expertise.
- Your app can identify objects, texts, people, scenes, and activities in images and videos.
- It supports features like facial analysis, visual search, etc.
- Amazon Rekognition supports a wide range of use cases like user verification, public safety, etc.
- You can use its comprehensive documentation.
10. Form your project team
Now that you have a project plan, you are ready to start. You need to form a development team. Do the following:
- Hire developers. Depending on your project scope, you might need to hire web developers, Android developers, iOS developers, and AI/ML developers.
- Hire UI Designers, testers, and DevOps engineers.
- Onboard your project team, which includes giving them the required access.
- Form a cohesive team. For Agile projects, you might want to build a “Scrum team”. These are cross-functional teams following the “Scrum” technique. “Scrum teams” focus on delivering values to the client at the end of each development iteration, called sprints. Such teams foster collaboration.
11. Develop algorithms for face recognition if you don’t prefer 3rd-party APIs
You might have unique requirements, furthermore, you might not like to depend on 3rd-party providers. Prepare to do the heavy lifting.
You need to do the following:
- Hire AI/ML programmers with expertise in popular languages like Python.
- Determine the kind of ML algorithms you need to develop. You would need to choose between supervised, unsupervised, semi-supervised, and reinforced-learning algorithms.
- Develop algorithms and test them against the data sets that you have acquired.
- Enhance the algorithms if required.
You need to create RESTful APIs for a scalable software development process. Your development team needs to do the following:
- Use cloud computing platforms to host the API back-end.
- Utilize relational database management systems (RDBMSs) like MySQL or PostgreSQL.
- Use NoSQL databases like MongoDB.
- Secure APIs using encryption, authentication tokens, quotas, throttling, etc.
- Design API endpoints smartly.
- Create effective rules for API requests and responses.
12. Develop, test, and deploy web and/or mobile apps
You now need to develop the proposed web and/or mobile apps. Do the following for web app development:
- Design the user interface (UI) by following appropriate guidelines.
- Use an “Integrated Development Environment” (IDE) like Eclipse.
- Develop the back-end using a suitable runtime environment like Node.js.
- Integrate APIs/SDKs.
- Test the app and deploy it on the cloud platform you chose.
Take the following steps for developing the proposed Android app:
- Design the UI by following the “Material Design” guidelines.
- Choose a programming language like Kotlin or Java.
- Use Android Studio, the popular IDE.
- Code the app and integrate APIs/SDKs.
- Test and deploy the app.
- Publish it to Google Play by following the Android developers’ guidelines.
Do the following to launch the proposed iOS app:
- Design the UI by complying with the “Human Interface Guidelines”.
- Use a programming language like Swift or Objective-C.
- Code the app using Xcode, the popular IDE for Apple’s platforms. Integrate APIs/SDKs.
- Test the app, and deploy it.
- Follow the “Apple App Store Review Guidelines” to publish your app.
Planning to Build an Identification App
This guide explains how to build an identification app using facial recognition technology. You might want to enhance it with the immutability and transparency of the blockchain. Use our guide to build a blockchain-based identification app.
Wondering whether you can get professional help while building an identification app? Take a few minutes and fill out a DevTeam.Space questionnaire. An account manager from DevTeam.Space will reach out to you.
FAQs on How to Build an Identification App
Building a face recognition app requires several AI capabilities including “Image Recognition”, “Computer Vision”, “Machine Learning”, and “Deep Learning”. These are niche technologies. Projects involving them are complex, therefore, you need an experienced team to build an identification app and similar software development projects.
DevTeam.Space has experienced AI developers with expertise in several key AI capabilities. Our developers have considerable experience in building a facial recognition system and other AI systems.
Reviewing the code helps to unearth defects earlier in the software development lifecycle. This helps you to meet your project schedule, quality, and cost objectives. You should plan for code review, and you can reach out to DevTeam.Space to find expert reviewers.