How to Integrate Facial Recognition Technology in the Mobile App?

A technology invented in 1967 has now reached our everyday using device-mobile phones. We are talking about Facial Recognition Technology (FRT). Though first used for policing, prevention, and security, we can now unlock our phones and even apps through FRT.

It uses a mixture of AI and biometric technology to identify the human face. The FRT technology has replaced long and complicated passwords and has made it easy for users to access the app. Such a technology adds another layer of security, ensuring the user’s data is safe.

Do you have an app without facial recognition technology? Then you must know that the Global FRT market is expected to grow to USD 9523.15 million by 2025. This is because ofsystem security, user safety, and better user engagement. Hence, integrating it into the mobile app is extremely important.

How does facial recognition technology work?

Facial recognition technology uses algorithms to analyze images or video frames of faces and compare them to a database of known faces to try and identify individuals. Here’s a general outline of how the process works:

Detection: The first step is to detect the face in an image or video frame. It can be done using various techniques, such as Haar cascades, which are classifiers that can detect the accurate location of objects in an image based on their features.

Alignment: Once a face is detected, the algorithm tries to align it to a standardized position by locating key facial landmarks, such as the eyes, nose, and mouth.

Feature extraction: The algorithm then extracts various features from the face, such as the distance between the eyes, the shape of the jawline, and the curvature of the lips. These features are used to create a numerical representation of the face, called a faceprint or face template.

Comparison: The faceprint is then compared to a database of known faceprints to try and identify the individual. It can be done using various techniques, such as Euclidean distance, which measures the similarity between two faceprints by calculating the distance between them in a high-dimensional space.

Verification of identification: Depending on the intended use case, the algorithm may either verify that the individual is who they claim to be (e.g., in a security checkpoint) or try to identify the individual based on their face alone (e.g., in a criminal investigation).

It’s worth noting that facial recognition technology has various challenges and limitations, such as variations in lighting and pose, as well as potential biases and privacy concerns.

How to implement FRT in your app

When applying face recognition in mobile applications, the biggest question is, which approach to use? There are various ways to implement, and these are:

1. OpenCV and Python

OpenCV is an open-source computer vision library, and Python is a popular programming language for ML. You can use OpenCV and Python together to implement facial recognition in the app. Here are the basic steps:

• Use OpenCV to capture images from the camera.
• Preprocess the images to extract facial features and align the faces.
• Use a machine learning algorithm (such as a support vector machine or a convolutional neural network) to train a model on the preprocessed data.
• Integrate the model into your app so that it can recognize faces in real-real-time

1. Native APIs

One of the easiest ways to create face recognition software for Android and iOS is through the help of Native APIs from Google and Apple. These are affordable, easy to implement, and require no extra cost or effort. Integrate the API in the app and ensure reliable picture detection and recognition features.

1. Microsoft Azure Cognitive Services

Microsoft Azure offers a suite of pre-built APIs that you can use to add facial recognition to your app. Here’s how you could use the Azure Face API:

• Send images to the Face API to detect and recognize faces.
• Use the Face API to identify facial features and attributes, such as age, gender, and emotion.
• Integrate the API into your app to recognize faces and display relevant information.

1. Google Cloud Vision API

Google Cloud also offers a facial recognition API that you can use to add face detection and recognition to your app. Here’s how you could use the Google Cloud

Vision API:

• Send images to the Vision API to detect and recognize faces.
• Use the API to extract facial landmarks like eyes and nose.
• Integrate the API into your app to recognize faces and perform related tasks.

It’s essential to remember that these are just some examples, and there are many other technologies and frameworks you could use to implement facial recognition in an app. Some additional ways of integrating facial recognition technology in the app are Amazon Rekognition, Dulux and loudAPI, etc. The choice of technology will depend on your specific use case, requirements, and expertise.

Wrapping up

To successfully implement face recognition, evaluating factors such as the type of recognition algorithm to use, data privacy and security concerns, user experience, and hardware requirements is essential. It is also important to conduct thorough testing and user feedback to ensure that the feature works effectively and meets user needs. With proper planning and execution, face recognition can be a powerful addition to any app, providing users with a seamless and secure experience.