Detection of human faces in real-time imaging is a common task, which may occur in different areas and fit diverse business demands. Various algorithms were developed to resolve this task, and some of these solutions can be used in mobile applications for iOS or Android.
Let us take a look at an iOS application prototype, which demonstrates the implementation of the Face Detection technology in quite a simple and entertaining way. Have a look at the following video to learn more about how you can use face tracking in mobile apps:
The existing solution might be used to build a turnkey and cost-effective application which meets your expectations and business needs. Contact us now to build the application of your dreams today! Discover more details about this cutting-edge application below.
Background on Face Tracking Technology
Face detection technology identifies human faces in digital images. Generally speaking, this is a basic technology in object-class detection, which has a wide array of applications.
However, the basic face detection technology can not be used for facial recognition (as this task is more complex and best solved by neural networks). This means Face Tracking applications are able to determine that a user’s face is in front of the camera but cannot tell exactly who the user is.
We use the features of the built-in iOS Core Image library to analyze and find human faces on an image. Its special CIDetector class is responsible for face detection, so the application gets comprehensive information about the face on the image. Developers may use this data to determine the size of the face and other important parameters.
Similarly, Android has identical Face Detecting capabilities built in. The Android.media API has a Face Detector class that brings all the features of CIDetector to the Google’s mobile applications.
Implementation of Face Tracking Features
Numerous mobile applications utilize Face Detection technology: ranging from custom-tailored camera apps to immersive eye-controlled games.
The face tracking feature helps keep the user’s face in focus in the majority of digital cameras. When the app detects someone smiling in front of the camera lens, it automatically takes a picture. Interestingly enough, the application range of Face Tracking technology can be significantly expanded to other areas. For example, the camera application can detect users’ faces and automatically take a picture when faces appear in the selected area. The application may even be able to apply various filters to the user’s face or simply blur the background.
Let us consider other possible applications of face tracking technology in mobile apps.
Would not it be great if you had a smartphone app that could automatically add likes to Facebook posts or add various emojis directly into the chat application when you frown or blink? With face tracking mobile apps this is entirely within reach.
Face tracking technology is even capable of detecting when drivers close their eyes or even drop their head and fall asleep. In this case, a loud sound will help to wake them up.
Another terrific feature brought to us by face tracking technology is the ability to automatically pause videos when the user turns their head away from the smartphone’s screen.
Finally, face tracking mobile apps can detect movements of user’s head to control the characters in the game.
Furthermore, facial detection is the first step in broader facial recognition procedures, which is a far more complicated task.
We have developed a simple iOS application prototype to demonstrate how Face Detection technology actually works in mobile devices.
Application Features
Users are free to add various masks to their faces detected by the smartphone’s camera. Masks are to be selected in the bottom part of the screen, and the in-flight modified video frames will appear above the application controls.
The application works simultaneously both on rear and front-facing cameras. It can detect one or more people and resize the masks according to the size of user’s face.
You can take a snapshot or even record a video with the masks added to your face. After some tuning one can use the modified faces in video streams, video calls and messengers such as Skype or WhatsApp.
Application Structure
The following diagram explains the application’s structure.
The CIDetector then processes the live camera stream, automatically detects when faces appear in front of the camera. Every detected face the CIDetector recognizes returns the following data:
- the coordinates of eyes and mouth,
- the flags indicating whether eyes are open or not,
- a special flag to detect the user’s smile,
- and the unique ID for further tracking of the particular face.
After that the application resizes the selected mask and calculates the right place of the screen to display it.
DB Best is always happy to develop the apps to accomplish specific goals. Integrating this proven solution into your app will make it cost-effective, but also very quick to adapt and release. Get a free consultation on how we can build a Face Tracking application to improve your business.
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