Modern technology has enabled the development of applications that can accurately analyze a user’s emotional response while they interact with websites or apps in real-time. In addition, devices such as home assistants are now capable of recognizing specific individuals and automatically tuning into their preferred TV channels.

Today, these technologies are no longer limited to security applications such as identifying criminals from surveillance footage or recognizing airline passengers. They have become an integral part of our daily lives, from facial recognition technology used to identify people in social media photos, to grouping images in Google Photos based on face recognition, and even unlocking smartphones with facial recognition. It is now relatively easy and cost-effective to develop custom face recognition apps for both iOS and Android platforms.

What does the term “Facial Recognition Technology” mean?

Facial recognition technology is a software-based biometric feature that uses patterns based on facial contours to exclusively recognize or verify an individual. There are several technologies involved in facial recognition, such as 3-D, vascular and heat-pattern, and skin texture analysis, to recognize someone using their facial features and appearances. In the most common form of facial recognition, algorithms identify certain points on the face and use them to create a template for that person.

This technology is most accurate when individuals voluntarily submit their images to a limited database. When an individual approaches a facial recognition scanner, their live face image is captured and converted into a template. This template is then compared to the templates stored in the database, and a match allows the individual to perform the desired action, such as passing through a door or logging into a computer network.

An Overview of the History of Facial Recognition

Facial recognition technology was first developed in the 1960s by Woodrow Wilson Bledsoe, who created a system to measure and categorize photos of faces. The system allowed new, unidentified faces to be compared against previously entered data points. Though the technology was slow by modern standards, it demonstrated the potential of the idea. Law enforcement showed interest in Bledsoe’s research in 1967, but his work was never published as a matching program.

Throughout the ’70s, ’80s, and ’90s, new methods such as the “Eigenface approach” and “Fisherfaces” improved the technology’s capability to detect and recognize facial features, paving the way for modern automated systems. However, facial recognition’s first significant public appearance in the US in 2001, when it was used on crowds at Super Bowl XXXV, sparked controversy. Critics argued that it violated Fourth Amendment rights against unreasonable search and seizure. In the same year, the Pinellas County Sheriff’s Office created one of the biggest local databases in the country for police use of the technology.

In 2008, Illinois’s Biometric Information Privacy Act became the first law in the US to regulate the collection and storage of biometric data, including facial photos. This law has been described as a model for commercial regulation by Jennifer Lynch, surveillance litigation director at the Electronic Frontier Foundation. In 2010, facial recognition became a standard feature as computers became powerful enough to train the networks necessary for the technology. The same year, facial recognition was used to confirm the identity of Osama bin Laden.

In 2014, Facebook introduced its DeepFace photo-tagging software, and facial recognition played a key role in identifying a thief in Chicago. That same year, Edward Snowden released documents that revealed the extent to which the US government was collecting images to build a database. Facial recognition first made its way into personal devices as a security feature through Windows Hello and Android’s Trusted Face in 2015. Later, in 2017, with the introduction of the iPhone X and Face ID, facial recognition became a more widely used feature in personal devices.

How Does Face Recognition Software Work?

Facial recognition is a technology that is often portrayed inaccurately in movies. In reality, every facial recognition system operates using a unique algorithm. However, there are three main types of technology that are used to build a facial recognition system.

Detection: The first step in facial recognition is detection. This involves locating a face in an image, which is similar to how cameras identify a face and automatically focus on it. Detection technology only focuses on finding a face and not the identity of the person.

Analysis: The second step is analysis, which maps out faces by measuring the distance between the eyes, the shape of the chin, and the distance between the mouth and nose. This data is then transformed into a sequence of numbers or points, known as a Faceprint. Social media platforms such as Instagram and Snapchat use similar technology for filters. While analysis may be prone to glitches and bugs, it becomes problematic only when the Faceprint is included in a recognition database.

Recognition: The final step is recognition, which is used to confirm the identity of a person in a photo. This can be used for security features on smartphones or to answer the question “Who is in this picture?” However, the disturbing side of facial recognition technology emerges during the recognition phase.

The detection phase begins with an algorithm that learns what a face is by being trained with photos of faces. With enough photos, the algorithm can distinguish between a face and an object like a wall outlet. Once the software has been trained to recognize and identify faces, it can then locate and compare them with other faces in a database. The software cross-references photos from a variety of sources to recognize a person and shows the results, which are typically ranked by accuracy. Although these systems may seem complex, it is possible to build a facial recognition system with standard software by someone with technical expertise.

How to Create a Mobile App With Facial Detection Technology?

When developing a face recognition mobile app, the biggest decision is which approach to use, depending on the project size and cost. The selection of the mobile platform should take into account the camera features and the opportunity to access and interact with it. However, to make the system work, you also need hardware assets such as cameras, servers, and other powerful devices. Therefore, it is essential to understand the fundamentals of creating a face detection app and where to start.

The Idea: The first step in building a face recognition app is to fully understand the concept and create a mobile app design. You need to ensure that your idea can be practically implemented and that you have the necessary technical expertise to do so.

Approach: It is also important to determine which approach to use when building a face recognition mobile app, taking into account the project size and budget. For popular operating systems like Android and iOS, there are open-source examples of algorithms and services for face recognition, as well as native and third-party options.

Native APIs: These are available for both Android and iOS platforms and offer accessibility and fast integration. While these APIs are limited in functionality, they can help reduce the final cost of face recognition mobile app development. Additionally, Apple and Google are continually improving their operating systems, so these APIs may be improved soon.

Third-Party Options: These involve services like Microsoft Azure Face API, Amazon Rekognition and KeyLemon Face Recognition API, or Cloud Vision API to develop hybrid applications. While these services are typically paid, they offer unlimited functionality and can recognize not only faces but also emotions and more.

OpenCV: (Open Source Computer Vision Library) is an open-source library that provides many algorithms for computer vision, image processing, and numerical algorithms of common purpose. The OpenCV Face Recognition library was created for the introduction and standardization of the common interface of computer vision and for promoting and increasing the number of applications in this field.

The Advantages of Using Facial Recognition in Mobile App Development

Facial recognition technology is gaining popularity in various industries, providing benefits such as enhanced security, improved user experiences, and streamlined processes. Let’s explore some of the advantages of facial recognition systems.

Security: Security is a major advantage of facial recognition technology. With mobile app developers using the technology to differentiate between human faces and photographs, users can protect their digital assets and private data. Popular face detection mobile apps such as FaceLock, LogMe, TrueKey, IObitApplock, FindFace, and FaceVault use facial recognition technology to enhance user privacy and security.

Pairing: Facial recognition technology can also be used to pair people with similar facial features for romantic purposes. Dating sites are developing applications that use facial recognition to suggest possible matches based on facial features.

Financial Transactions: The technology is also beneficial for financial transactions, as reliable smartphone manufacturers like Apple and Google have installed facial recognition software in their devices. Users can easily conduct financial transactions without remembering complex passwords, while on the go.

Healthcare: In the healthcare industry, medical professionals can use facial recognition technology to detect a patient’s illness by analyzing facial features. Facial expression recognition can help doctors detect symptoms such as swelling or inflammation, and provide the best course of action for the patient.

eCommerce: Facial recognition technology is also being used in eCommerce and retail industries. For instance, Warby Parker uses facial recognition on its website, allowing customers to virtually try on glasses frames by uploading a picture of their face.

Conclusion:

The future of facial recognition technology is promising, with analysts predicting significant growth and revenue generation in the coming years. The technology will continue to transform security and surveillance, among other areas. In conclusion, facial recognition technology is making our lives easier by providing convenience and improving user experiences.

To leverage the technology, it’s recommended to hire a reputable mobile app development company like Gsquare Web Technologies to ensure quality results.