Based on estimates, the face recognition technology market is projected to develop at a 14.8% compound annual growth rate (CAGR) between 2020 and 2027. FRT is one of the biometric technologies with the quickest rate of growth; by 2027, the market is projected to reach $12.92 billion.
Originally developed as surveillance equipment to track down criminals and assist with border control, this technology is now being offered as a tool to guarantee security, boost consumer loyalty, amuse users, and much more.
It is equally true, however, that there has always been disagreement over it. Although some see it as a danger to social rights, others regard it as a potentially useful invention that will make many duties easier. Whichever side you support, there's no denying that facial recognition technology, or FRT, has grown in importance across a wide range of businesses.
It is used in a variety of software applications, including fleet management, retail, security, and surveillance apps, as well as mobile application development. You've come to the perfect spot if you want to include facial recognition technology into your company mobile app or are going to build face recognition software yourself.
This blog post will cover everything there is to know about facial recognition technology, including its definition, advantages, and applications to improve user experience for mobile apps. Now, let's get going.
Why Is There a Growth in Face Recognition Technology?
It's true that one of the trickiest and most contentious technologies to work with is face recognition. Nevertheless, the facial recognition startup industry is growing rather than contracting.
What, however, is causing everyone to go insane about FRT? Every day, entrepreneurs come up with fresh concepts for creating solutions based on FRT, and investors flood the market with funds. The evidence is seen in mobile applications like AnyVision and Jumio, which announce fresh investment rounds on a quarterly basis.
We have the explanation for WHY, if you're curious. Every sector may benefit from facial recognition technology, which is why people are eager to test it out in order to enhance current company procedures and create fresh, creative solutions.
Additionally, FRT is changing dramatically as a result of its expanding use in a number of businesses and areas. It adds visual biometric identifying capabilities to a wide range of applications. Because of this, face recognition technology is used for a wide range of tasks, such as:
Verification of identification with biometrics
The automated checkout procedure at retail establishments
Workforce management and campus security
Client confirmation
Identification of the patient
Savvy banking
Enhanced safety for the general population
Visual search's capacity to find products
Creating marketing initiatives with a specific focus
Making genetic disease diagnoses
Fraud prevention and store security
conducting ATM cardless transactions
Food Image Identification
Increasing social media user engagement
The use of face recognition technology has grown significantly during the last several years. Apple introduced Face ID in 2017, which was the first sign-in feature to use FRT. At the moment, between 15 and 20 percent of American financial institutions, including banks, utilize face recognition technology to authenticate papers and confirm individuals' identities.
According to a NIST research, face recognition software and systems were found to be 99% accurate when looking up images in a database. Additionally, according to Bloomberg, the worldwide FRT market is expected to develop at a 21% CAGR and reach USD 11.62 billion by 2026.
It is evident from these figures and industry trends just how rapidly FRT technology is developing. Let's discuss how to put face recognition software into practice if you want to build it or utilize it to solve a business issue.
How Can Your Software or App Use Facial Recognition Technology?
Make sure you fully comprehend the notion before investing in the creation of face recognition apps. How should anything that you want to construct operate? Here are three methods to include FRT into your mobile application after you've got it working:
1. Employ native APIs for face detection
A mobile application may be created for iOS, Android, or both platforms. To assist you in incorporating face recognition functionality into your app, these platforms provide their own APIs.
With these solutions, you may drastically cut the cost of developing a face recognition software in NYC, even if the functionality these APIs provide will be restricted. To guarantee dependable photo detection and identification capabilities, all you have to do is include the API into your application.
The primary benefit of using native APIs is their multi-device optimization and hardware acceleration component enhancement. For instance, Apple offers an API for computer vision-related topics called Vision API. It has detectors for barcodes, text, and faces.
2. Examine the OpenCV Library
This is the library that is often used to create applications for face recognition. It facilitates the integration of ML and computer vision into the applications. In essence, OpenCV is an object detection platform that supports a variety of techniques and aids in the recognition of all kinds of things. In many situations, this library performs well for face detection and identification.
OpenCV was first developed to standardize the computer vision industry's generic interface and to facilitate the creation of new models. Its primary benefit is that using it is free. Its integration with mobile apps is a little more difficult, however, particularly with Android's facial recognition. It requires in-depth knowledge.
Select a Third-Party Approach
Numerous options are available on the market to provide a seamless and very comfortable development procedure. Third-party services such as Amazon Rekognition, Microsoft Face API, Google's Cloud Vision API, Kairos, and many more may be used to enhance your business processes using FTR or to include facial recognition into your app. These services are usually paid for, but they provide incredible functionality by identifying emotions, ethnicity, and a host of other characteristics in addition to faces.
Every third-party solution will come with pros and cons of its own. As a result, you will need to make a very thoughtful decision depending on your budget and company needs. In the next part, let's examine some of the top-rated and most often used APIs for implementing FRT.
The Top 4 APIs for Adding Face Recognition to Your Mobile App
APIs for mobile applications directly support the expansion of businesses. Therefore, using an API for implementation is the ideal option if you're going to create mobile apps or face recognition software. By boosting developer productivity, APIs enable companies to provide creative solutions at a lower cost.
The top face recognition APIs are as follows:
1. The Face API from Microsoft
The Microsoft face API was first made available in 2016 and is a component of Microsoft's Cognitive Services. Although it is based on one of the most sophisticated face recognition algorithms ever created, it is not without flaws. Facial recognition, detection, and emotion recognition are the API's primary features.
It lets your mobile app recognize and categorize faces that are similar to each other while forecasting the emotions connected to those faces. For instance, Uber, a first-rate ride-hailing service, verifies the identity of its drivers in real time by using Microsoft's Cognitive Services.
Users just need to snap an in-app selfie, which is then used to compare with the driver's database entry to determine who the driver is. Uber performs this verification to safeguard the driver's account. But there are also some restrictions with the API. It isn't included with an SDK, which restricts the versatility available to developers and excludes ethnicity identification.
2. The Face Recognition API from Kairos
The fast and secure face identification offered by Kairos API is based on deep learning techniques. It has comprehensive documentation, is simple to use, and includes a handbook to assist you in designing a positive user experience for your face recognition software.
Kairos further offers an SDK and an API for face recognition. Their technology can identify gender, age, emotions, and ethnicity in addition to detecting faces and working with both photos and videos. Since Kairos APIs allow important data to be offloaded to the cloud, the firm provides improved security, privacy protections, and audits for usage in the business world.
For optimal control, privacy, and security during integration, you may host it on your own servers or use their cloud API. The following are a few characteristics of the Kairos platform:
Face recognition, authentication, and confirmation
Age and gender identification
matching of facial coordinates
Anti-spoof identification
Diversity Appreciation
3. Recognition API for AWS
The foundation of Amazon Rekognition is deep learning technology that has been tested and scaled to analyze billions of photos and videos. This technology was created by Amazon scientists. To utilize this, you do not need to be an expert in machine learning.
This user-friendly API can quickly analyze any image or video saved on Amazon S3. For improved face recognition and comparison, Amazon continuously adds new characteristics and labels to the service as it learns fresh data.
Highly accurate face analysis, face search, and comparison features are offered by Amazon Rekognition. Face detection, comparison, and analysis are available for a number of use cases, such as counting, user authentication, public safety, and cataloging. Among the typical applications for the Amazon Rekognition API are:
Image and video collections that may be searched
Analysis of sentiment and demographics
Identifying personal safety equipment
Face search and identification of harmful material
Text detection and celebrity identification
4. The Face Detection API for Google Cloud Vision
The Google Cloud Vision API offers a quick and dependable option as it is a component of Google's cloud platform. However, facial recognition is not yet supported; it is just accessible for face detection. Through the AutoML vision application, Cloud Vision provides customizable models and pre-trained models over the API. A progressive web app framework leverages modern web capabilities to deliver app-like experiences on the web, providing offline access, push notifications, and faster load times to enhance user engagement and performance.