Machine learning helps computers to solve tasks that have, until now, only been performed by humans.
Machine learning is driving an explosion in the capabilities of artificial intelligence, from offering security solutions to monitoring patient medication – helping software make sense of the messy and unpredictable real world. Data, and lots of it, is the key to making machine learning work. While several machine learning algorithms have been around for a long time, a recent breakthrough is the ability to apply complex mathematical equations to big data automatically.
Two key areas where machine learning has grown exponentially are the inclusion of facial and voice recognition in a mobile application.
How Facial Recognition Technology Works
By providing an image or video of an individual, facial recognition software identifies and authenticates them through using a set of recognisable and verifiable data unique and specific to that person. The process of facial recognition is usually defined as a five-step process:
- Facial detection and tracking,
- Facial alignment,
- Feature extraction,
- Feature matching,
- Facial recognition.
Feature extraction involves obtaining relevant facial information to determine whether the object is human. Finally, the system tries to recognise the face and match it to a name stored in the database.
Facial biometrics continues to be the preferred biometric standard used. That’s because it’s easy to deploy and implement. There is no physical interaction required by the end-user.
Additionally, A study published in June 2019, estimates that by 2024, the global facial recognition market would generate $7billion of revenue, supported by a compound annual growth rate (CAGR) of 16% over the period 2019-2024.
Public sector surveillance and numerous other applications in diverse market segments are the two most important drivers of this development.
According to the study, the top facial recognition vendors include Accenture, Aware, BioID, Certibio, Fujitsu, Fulcrum Biometrics, Thales, HYPR, Idemia, Leidos, M2SYS, NEC, Nuance, Phonexia, and Smilepass.
What Is Facial Recognition Used For?
The main application categories where facial recognition is being used, are for security and law enforcement, health, banking, and retail.
Significant advances have been made in the health category. Thanks to deep learning and face analysis, it is already possible to track a patient’s use of medication, support pain management procedures and detect genetic diseases.
Similarly, digital account opening (DAO) is one of the most popular technologies used in banking and this important trend is being combined with the latest marketing advances in customer experience.
Furthermore, by placing cameras in retail outlets, it is now possible to analyse shoppers’ behaviour and improve the customer purchase process.
2021’s Top 10 Face Recognition Apps:
Creating A Mobile App with Face Recognition
There are many options available and below we’ve listed the 3 most popular of them.
Using Apple and Google Native APIs is the best way to build face recognition apps for Android or iOS. It is affordable, easy to execute and needs no additional costs or effort.
The second option is to use third-party solutions. The range of choices of these tools would surprise you: many businesses sell their APIs to build a face detection app. The cost of face recognition mobile apps can vary vastly in the case of third-party solutions.
OpenCV (Open Source Computer Vision Library)
OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. It was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in commercial products. Being a BSD-licensed product, OpenCV makes it easy for businesses to utilise and modify the code.
What Is A Speech Recognition System?
Speech recognition is any system in which audio is taken in and attempts to recognise and understand speech within it. Voice recognition may provide an alternative to keyboard typing. An external microphone, headset or built-in microphone is spoken by the user and their words appear on the screen as text.
How Does A Speech Recognition System Work?
It breaks down the sounds as the computer listens to the human voice in such a way that it can understand individual words. To enhance their accuracy, more sophisticated programs use machine learning. Such systems are capable of learning accents, distinct pitches, voice tones, etc.
Mobile Apps That Use Speech Recognition:
- Google Mobile Apps – on Android, BlackBerry, and iOS – Free
- Bing – on Android and iOS – Free
- Siri Assistant – for iOS – Free
- DriveSafe.ly – for Android, BlackBerry, iOS – $13.95 per year
- Dragon Downloadable Apps – on Android, BlackBerry, iOS
- Jibbigo Voice Translation – on Android, iOS – Free
As we continue to see facial recognition become more prevalent, we will see convenience increase for users. After all, isn’t the whole point of technology to make our lives easier?
You now know everything you need to create a custom face recognition app. The remainder is up to the developers you’ll be hiring. You should discuss your scope with them, draw up a plan, and start working on the project.
Make sure that you engage an experienced team capable of overcoming any challenges. The app development firm with which you collaborate should be prepared to face all the challenges of developing a mobile face recognition application.
Appello is increasingly working with clients that want to add facial recognition to their mobile apps. We pride ourselves in developing cost effective, custom built mobile applications, tailored to your specific business practices and management needs. Contact us today.
Recently, Appello’s now Chief Project Manager Shoghik, wrote about the 5 tech trends to look out for in 2021. Read it here.