Are you someone who wants to create marvellous mobile apps with facial recognition? Being a mobile app development agency, it is fairly important to keep up with recent trends. Although the field of facial recognition is quite new, early adopters have managed to grab considerable profits from it. The sole idea behind this article is to make you aware, how to incorporate facial recognition with your mobile app and implement the same.
Creating a Mobile App with Facial Recognition
Your approach towards building the mobile app must depend on the size of the project and the overall cost. Since facial recognition is completely dependent on mobile hardware such as the camera, consider the possibilities that you can put forward on the table.
There are a plethora of options to choose from when it comes to using the right algorithm. At the end of the day, the testing and development setup for iOS is going to differ from that of Android. Down below is a list of some of the widely used options to choose from.
1) Native APIs
Both Android and iOS come with their own sets of Native APIs, which were mainly created, keeping convenience and ease of use in mind. The only drawback is that these APIs are limited in functionality. On the contrary, one can easily cut down a huge amount from their final cost. That being said, the two major companies responsible for the two operating systems, i.e. Apple and Google, are also constantly updating their operating system.
2) Third-Party APIs
Competition against native APIs has also come a long way. For starters, Microsoft has come up with its own Azure Face API, while Amazon has Rekognition. Although these services come with a higher price tag, you can easily get the value for the buck. That being said, these APIs are not only beneficial to recognize face but also facial expressions to determine the emotion.
The term OpenCV stands for Open-source Computer Vision Library. It is a bundle of different algorithms, including computer vision and image processing. Although it is completely free to use, you will have a hard time implementing the algorithms for mobile phones. That being said, if you have a good stronghold on computer vision technology, you will know your way around OpenCV.
How Does Facial Recognition Software Work?
In Layman terms, facial recognition is nothing but an enhanced version of image analysis. While the input is taken as an image or video feed, the verification process turns out to be the output. Down below is a list of the most generic way in which a facial recognition software work:
- First, the image is analyzed to find the presence of face
- The unique features are highlighted to ensure whether the test image matches with the reference one
- Like every person has their unique fingerprint, the same goes for their face print
- Lastly, a detailed comparison between the two images is made, ending the verification process
Algorithms Used for Facial Recognition
We have already discussed the various procedures that go into making a mobile app with facial recognition. Here you will find some of the most commonly used algorithms for face recognition.
Principal Component Analysis
One of the most elaborate algorithms that are used for facial recognition is Principal Component Analysis. The algorithms are mainly responsible for representing the input images into small dimensional vectors. These are then compared with the database, where the benchmark vector resides.
What makes PCA a superior algorithm is that it can easily figure out the difference between various faces while significantly reducing the overall dimensionality. As described earlier, the input images are converted into vectors, and further into the set of linear coefficients, which are termed as eigenfaces. The only downside is that it cannot differentiate between facial expressions.
Artificial Neural Network
The artificial neural network is yet another popular algorithm that is heavily used in facial recognition. Python is used in AI and ML to add better features and decision making. The pre-trained neural network classifies the input image through a multi-layer perceptron.
The entire algorithm is based on the learning curve. For instance, during the training procedure, all networks can extract all the key features, their importance and even build up a relation between them. Although training a neural network can get time and resource consuming, they can significantly improve the facial recognition result along with limited errors.
The combination of the artificial neural network along with principal component analysis can always provide a very promising result. While the facial inputs are taken in the form of eigenfaces, a neural network can actually come up with the right learning curve and classify the descriptors.
Advantages of Adding Facial Recognition
Everyone can relate that facial recognition is not new anymore. But, there is a huge potential for it in the foreseeable future. Down below is a list of some of the driving factors for implementing facial recognition with your mobile app:
• Added Security
One of the heavy usages of facial recognition is with system authentications. Mobile apps built to provide logistic supply chain solutions, can implement facial recognition for added security. For example, even Amazon has its own authentication method termed as “Image Analysis for User Authentication.” What it does is that it enables the users to just smile or wink in front of the camera, thus providing an identification confirmation.
• User Safety
Ensuring user safety must come above everything else. For Starters, the company Caterpillar is using facial recognition to make sure that drivers don’t fall asleep. There is a special program that actually accesses, whether a driver is tried or not. Such implementation is done just by keeping track of the position of the eyes and head. The same idea can also be used to build productive mobile apps.
• User Engagement
Restaurants, hotels, and even cafes can make use of facial recognition to improve their customer loyalty. For example, its system software can easily recognize any retaining customer and can then welcome them.
These are some of the most common usages of face recognition; we have seen so far. So, mobile app developers should never underestimate the growing demand for the technology, as implemented at the earliest hour possible.
Facial recognition is one of the most promising technologies that is growing side upside with fingerprint detection. It has a huge array of applications while raising the level of security of the mobile app. As stated earlier, facial recognition can add the touch of versatility to your mobile app. So, it’s the best time to put your thoughts on the next great mobile app with facial recognition.
James Grills is currently associated with Cumulations Technologies, a Flutter app development company. He is a technical writer with a passion for writing on emerging technologies in the areas of mobile application development and IoT technology.