The facial recognition process works the same way the human mind functions. It employs biometrics to map face traits from images or videos. Then compares the information with a database to confirm the user’s identification. The market is rapidly increasing. According to Statista, the facial recognition market is estimated at 3.8 billion U.S. dollars in 2020. By 2022, it is expected to be worth more than $7.7 billion. These apps are now available in a variety of commercial formats, ranging from identity verification to marketing.
The facial recognition software market is projected to grow, reaching 8.5 billion U.S. dollars by 2025. Click To Tweet
Woodrow Wilson Bledsoe is widely regarded as the father of facial recognition. In the 1960s, Bledsoe developed a technique that allowed users to manually categorize images of people using the RAND tablet. With the use of a stylus that produced electromagnetic pulses, people could enter vertical and horizontal coordinates on a grid on the tablet. That approach was used to manually capture the coordinates of facial characteristics such as the eyes, nose, mouth, and hairline.
Metrics that were manually recorded could be preserved in a database afterward. When a new photograph of an individual was entered into the system, the database was able to find the most similar image. Face recognition was unaffected by technology or computer processing power throughout this time. Nonetheless, it was Bledsoe’s first and most important step in demonstrating that facial recognition could be used as a biometric.
The future of facial recognition technology seems bright till now.
In the following years, the technology is likely to grow and generate significant earnings. The major businesses that will be heavily influenced by technology are surveillance and security. For better administration, schools, universities, and even healthcare facilities are aiming to deploy facial recognition technology on their premises. Facial technology’s complicated technology is making its way into the robotics business.
The fact that machines can now reliably recognize individuals opens up a host of possibilities for the security industry. The most important of which is the capacity to detect illicit access to areas where non-authorized people shouldn’t be.
Facial recognition could be used in fleet management to give alerts to unauthorized individuals attempting to obtain access to vehicles, preventing theft. This technology has the potential to work in tandem with the advent of self-driving automobiles, which is fascinating. What if cars could react to robbery attempts in order to prevent theft of the vehicle or its contents?
This system might be taught to detect when a driver’s eyes are not on the road, given that inattention—largely due to smartphone use—is the greatest cause of accidents after drinking and speeding. It may also be trained to detect eyes that indicate an inebriated or tired driver, improving the safety of fleet vehicles.
As far as the sharing economy is concerned, facial recognition acts as another layer of protection to rides-both the driver and the passengers.
Grab, a Southeast Asian ride-hailing business that bought Uber’s Southeast Asian stake and exited the market has teamed up with Microsoft to use face recognition technology to precisely identify the right drivers and passengers for each journey. This offers an extra layer of protection, giving commuters peace of mind in a market that isn’t usually known for passenger safety.
In the future, cars may be equipped with built-in face-scanning technologies, allowing for enhanced security without the use of a separate device. It can even recognize faces, unlock vehicles, and allow customers to join only if they were the ones who booked the ride. This will provide the highest level of security possible. Users won’t even notice security that smoothly interacts with user behaviors.
Once again, a security-driven facial recognition application makes its way into the IoT sphere. Facial recognition is most commonly used in houses on intrusion detection systems, which identify if someone enters a home when an intrusion alarm is left armed.
Enhancing the capabilities of existing intrusion devices to expand into access control devices could be a step forward in this industry. Doors could unlock automatically when residents arrive at their front entrance in the future. This will eliminate the need for traditional door locks and fumbling with keys, or relying on mobile devices to manually unlock today’s “smart” lock systems.
Face recognition could allow companies to capture what customers are looking at in physical stores. This allows “offline” purchase patterns to be carried over to the internet. It simply translates to better insights and analytics about their clients’ purchase behavior.
The retail industry acknowledges that facial recognition is the next stage in their ongoing effort to personalize shopping experiences for customers.
Unmanned, automated immigration clearance gates are likely something you’ve noticed and even loved. Facial recognition technology is used at work to protect our borders and keep them secure in a number of ways, which you probably didn’t notice.
The ability to recognize and impede border crossings by known criminals and individuals of interest using facial recognition is one of the most important. Today’s border controls use information databases, such as INTERPOL’s ‘Facial Identification’ approach, to identify people against a scale of accuracy. Processing facial data in the cloud also opens up the possibility of running predictive algorithms over the footage to account for things like aging, plastic surgery, cosmetics, and even the effects of drugs, in addition to traditional image upgrades.
Face recognition technology is being used by both government and business organizations for a variety of purposes. Initially, it was exclusively used by security organizations or government departments to check for security purposes. But as time went on, it became more widely employed.
This technology began to pervade industrial verticals once the iPhone made it a household term.
You can also read this previous blog about how augmented reality integration with on-demand beauty apps can improve the salon experience here.
The novel technology uses an algorithm to interpret the face as data that can be stored, examined, and compared as needed. Here are the basic steps for using the app:
The powerful technology varies according to the application and the manufacturer’s preference, but it generally follows a standard pattern that includes capturing, processing, analyzing, and comparing with the database record. These are the steps:
The first step is to develop a scientific understanding of face recognition
The second step is to build a system that can acquire and match faces from photographs.
You need enough data that the model can learn to get accurate results.
You need to translate your high-level representation into a means that drives the facial recognition engine.
Deep learning is a subset of machine learning. Deep Learning is modeled after how human intelligence works and is made up of artificial neural networks. It works in a similar way but on a much more complex and deep level.
Collect a large dataset of facial images and label them with each person’s name or identifier.
Train your model (deep learning) by showing it the labeled images and telling it which is which.
While the above information will give you a brief overview of the development process, the following steps will help you get a better understanding of the process.
You need to build a database of pictures (at least 5000 for the most accurate results) using facial-recognition software.
If you already have pictures, you can use them as well, but make sure they are taken from different angles and distances.
One way that artificial intelligence can be used to build a database of pictures for this type of software is by having an artificially intelligent computer program analyzes all the pictures on social media. Then create a database with them categorized by which person’s face was in the picture.
Building facial recognition software is not as easy as it sounds. That is why we need to train the software by inserting new pictures into the database. The system will be able to learn and identify images of faces and can then compare them with other images that we haven’t trained it with.
To build a good system, we need to start by taking a picture of a person’s face. Then extracting the most important facial features like the eyes, nose, mouth, and eyebrows.
We can extract various data points such as distance between specific points on the face or angles of the face etc., which are important for identifying different faces.
Building this AI-powered system may seem complicated at first glance. But if you break down each step into smaller tasks it becomes much easier.
This will help to avoid errors during identification. The algorithm will learn from your pictures to identify others with similar features in future use cases.
Train the algorithm with the survey data to make it more accurate in recognizing faces when it is in “live” mode (recognizing faces when they are in real-time).
The first thing to do is to create a training set of faces that are labeled with names. Then, train your algorithm on the data. The goal is to create an algorithm that can correctly identify people in an image or video frame by matching the face with the name associated with it.
The main issue that needs to be addressed before the facial-recognition software can be used as an identification tool is its accuracy. There are several issues with the accuracy of the facial-recognition software such as its inability to recognize faces of people who are not white, different skin tones and there is also an issue of false positives. So, there need to be some changes made in order to improve its accuracy.
In order to build facial recognition software that is accurate enough for use as an identification tool, it should have layers of data to compare with. In comparison with one-layer recognition where it only has one reference photo available for comparison. Two-layer recognition has two reference images available for comparison.
For facial-recognition software, accurate identification of a face is crucial. This accuracy ranges from 99.9% to 100%. The accuracy of the software depends upon the resolution, angle, and lighting conditions.
In order to make sure that the accuracy of this software is high, we need to make sure that it can work in any lighting condition and from any angle. The best way to do it is by training the AI with some facial photographs from those angles and lighting conditions and then testing it on live events once again after this training.
This is to evaluate how well your algorithm performs on a live-streamed video of faces. Most of the time, the event organizers are required to give an estimate of how many people will be attending their event in order to manage the capacity. This program will be used to count how many people attended and make sure that there is enough capacity.
Live video streaming has become a popular way for people to watch their favorite artists in action in real-time. Facial recognition software can be used for this purpose by counting how many attendees are at live events.
Like any other technology, facial recognition software has its fair share of pros and cons. We will discuss it here.
Because every person who enters your site will be accounted for, a facial biometric security system can significantly increase your security. Trespassers will be detected instantly by the identification system, and you will be notified immediately. You may be able to save money on security staffing by using a facial recognition security system.
Face ID technology is becoming more and more reliable with today’s technologies. Because of advances in 3D facial recognition technologies and infrared cameras, the success rate is currently at an all-time high. The combination of these technologies makes fooling the system extremely difficult. You can be confident that the premise is more secure and safe for you and your colleagues with such precision.
Previously, security personnel was required to certify a match and ensure that the system was working properly. As previously said, technology has progressed to the point where this is no longer essential. Face recognition technology can now totally automate the process while also ensuring high accuracy. This translates to convenience and cost savings.
Data storage is gold in today’s world of data since there is so much of it. Everything demands room, whether it’s a high-definition movie or 100,000 faces to store. This means that, in order for facial recognition systems to be effective, only roughly 10% to 25% of videos are processed. To overcome this, many businesses employ a large number of computers to process information and reduce processing time. However; until technology significantly develops, this obstacle is here to stay.
The camera angle has a significant impact on whether a face gets processed or not. A facial recognition system must be able to detect a face from a variety of angles, including profile, frontal, 45 degrees, and more, in order to produce the most accurate matches. In addition, any obstacles, such as facial hair or headwear, can be problematic. It’s vital to keep the database updated and up to date with its data. This is in order to avoid any setbacks or failures.
The cost of installing facial recognition systems might vary greatly, but three factors virtually always play a role: business logic creation, integration with facial recognition solutions, and product prices.
Consider the given use case of an attendance system when developing business logic. What do you do with the information about who is present once you have it? Do you just save it in a Google Doc or do you build a complicated online application that includes the users’ roles?
The business logic varies a lot based on your use case and requirements, and it has a big impact on the cost. Integrating with a facial recognition solution is the next cost. This cost does not vary greatly amongst solutions, but it is influenced by your requirements and individual use case, just like business logic development.
Integration with facial recognition technology, on the other hand, can be reasonably straightforward if your business logic is well-planned and built. The final cost is incurred during the manufacturing process. Users of face recognition systems are frequently surprised by this expense. They tend to focus on what they need to spend to get the system up and running, but they don’t always include the ongoing costs of putting the system into production. It can be costly, so making sure the system provides a long-term return on investment is critical. The typical cost starts from $5000 USD and goes up in addition to technical and functional requirements.
As the number of applications for facial recognition grows, so does the possibility to create and deploy these systems. Face recognition is becoming a viable alternative for many businesses. This is thanks to advances in technology as well as artificial intelligence and machine learning solutions. Companies must tread carefully, though, because the costs of facial recognition remain quite high. We anticipate that costs will continue to reduce in the near future, and that, when combined with ever-improving technological capabilities, facial recognition will become a more prevalent technology.
Idea Usher has years of experience in developing complex software technology. If you believe that facial recognition technology is the future. And if you feel like investing in this sector, then now is the right time. Get in touch with our expert developer for further insight and intricate details now.
A Skilled Developer For Every Project
Trusted by 100+ clients
Best Facial Recognition Software Deep Vision AI, SenseTime, Amazon Rekognition, FaceFirst. Trueface. Face++ Kairos. Cognitec.
Facial Recognition in public safety. Facial recognition is one of the easiest and most commonly used biometric tools. There have been two major ethical concerns: development bias and facial recognition ethics of use. In the former, facial recognition must be developed before it can be implemented.
Developed in the 1960s, the first semi-automated system for face recognition required the administrator to locate features (such as eyes, ears, nose, and mouth) on the photographs before it calculated distances and ratios to a common reference point, which were then compared to reference data.
Idea Usher is a pioneering IT company with a definite set of services and solutions. We aim at providing impeccable services to our clients and establishing a reliable relationship.
Hi 👋 Can I help you?
Leave a Comment