The app that recognizes faces no longer sounds like a miracle. Although it used to sound so, and in the recent past.
The facial recognition market is anticipated to grow to $7.7 billion in 2022 from $4 billion in 2020. That’s because facial recognition has all aspects of marketable applications. It can be used for everything from surveillance to marketing.
In the COVID-19 outbreak, contact outlining through biometric identification has become the widely embraced tool to curtail the virus spread. From surveying temperatures to identifying people without masks, Artificial Intelligence (AI) has played a major role in various countries including facial recognition into their strategies and replacing it with contact biometric systems.
What is facial recognition technology?
So, what is a face recognition system? We are speaking about an online or mobile application able to separate a human face in an image or video and even specify the identity of a person it belongs to.
In other words, Facial recognition is a biometric identification system to identify, verify, and authenticate the person using facial features from any photo or video. Facial recognition system works on correlating facial biometric structures of the face of interest with the database of known faces to find the match.
Current face recognition apps identify such attributes as:
- Sex of the person;
- His estimated age;
- Emotional state.
Why Is Face Recognition On The Rise?
The technology of facial recognition is not new, but it foresees new growth opportunities in the coming years. The major factor driving progress is an unlimited number of ways Machine Learning Services came to and employ facial recognition in various business areas. Here are just a few examples:
Facial recognition can be widely applied as a security authentication method. Not so long ago, Amazon invented an authentication method, known as “Image Analysis for User Authentication”. It enables users to make a transaction by performing a certain action (a smile or wink) in front of the camera, thus confirming their identity.
Caterpillar has applied facial recognition technology to solve the problem of sleepy drivers. A special program assesses how tired a driver looks. By constantly analyzing the position of their eyes and head, the system can decide whether the plant operator needs a wake-up call.
One way to increase the loyalty of customers to cafes, restaurants, and hotels is by using facial recognition technology to welcome them in person.
How Our Face Recognition Application Works
The facial recognition application works on the face reasoning and judgment principle, where it detects and intercepts the face with the images already bottled in the database. If both the samples match, it results in face authentication success.
Face recognition app process: –
- Captures or Face Detection
Initially, the system apprehends or captures some image samples from various angles and stores them in its database for forthcoming identification intentions. The face is best detected when the person is looking directly at the camera. The technological advancements have enabled slight variations from this to work as well.
- Face Analysis
Next, a photo of the face is captured and analyzed. Most facial recognition relies on 2D images rather than 3D because it can more conveniently match a 2D photo with public photos or those in a database. Distinguishable landmarks or nodal points make up each face. Each human face has 80 nodal points. Facial recognition software will analyze the nodal points such as the distance between your eyes or the shape of your cheekbones.
The face recognition app distinguishes the newly given sample from the already existing database template. The analysis of your face is then turned into a mathematical formula. These facial features become numbers in a code.
If both the existing template and recently passed template match accurately with each other, the face confirmation is achieved or else denied. The technology then identifies a match for your exact features in the provided database. It returns with the match and attached information such as name and address.
Features of a Face Recognition Software using Deep Machine Learning.
- Fast face processing
The biometric face recognition system operates fast and precise detection of faces in live video streams. The face on the modern frame is detected in 0.07 sec. and then each face is processed in 0.13 sec.
- Live face detection
A basic face identification system can be effortlessly fooled by positioning a photo of another person in front of a camera. face recognition system with the new machine learning can stave off this kind of security breach by specifying whether a face in a video stream belongs to a real human or is a photo.
- Face image quality determination
A quality boundary can be used during face enrolment to assure that only the best quality face template will be stocked in the database.
- Tolerance to face posture
A face recognition system has specific tolerance to face manners that assures face enrolment convenience: rotation of a head can be up to 10 degrees from frontal in each direction (shook up/down, rotated left/right, tilted left/right).
- Multiple samples of the same face
Biometric template records can comprise multiple face varieties belonging to the same person. These samples can be enrolled with various face postures and expressions, from distinct sources at varied times thus enabling to enhance matching quality.
- Identification capability
Face recognition system functions can be utilized in 1-to-1 matching (verification), as well as 1-to-many mode (identification).
- Fast face matching
The JUSTLOOK 3.2 face template matching algorithm distinguishes 100,000 faces per second. The compact face features a template. A face features template occupies only 2.3 Kilobytes, thus applications can deal with large face databases.
- Features generalization mode
This feature yields the collection of generalized face features from numerous images of the same subject. Therefore, each face image is filtered, features are extracted, and the collections of features are evaluated and incorporated into a single generalized features collection, which is jotted down to the database. This way, the enrolled feature template is more durable and the face recognition quality improvements extensively.
Popular Use Cases of Face Recognition App Development-
AI-based Face Recognition Software offering services in different industries. Such as-
Banking & Finance
By employing facial recognition software, banks can furnish a better customer experience by recognizing the key customers. They can have a promising insight into the client-manager relationship and bid finer services.
In educational institutions, face recognition software can track the break timings of teachers, primarily when they take longer. This is a powerful technique to keep an eye on any time wastage done by teachers. With this software, the attendance of students can also be tracked down without any manual mishaps.
Airports are most likely to have criminal and terrorist activities. AI Face Recognition system incorporated with surveillance cameras support in recognizing a terrorist who might have been accused in some horrible activity.
Artificial Intelligence (AI)-based Face Recognition systems can rapidly identify a criminal, shoplifter, or customer with deceitful history in the retail sector. The administrators can be instantly informed when the suspicious enters the store.
Facial Recognition Solutions aid in identifying former guests while they enter the hotel premises. This method is very effective in comprehending the admirable customer’s preference-related information.
AI-based face recognition software can diagnose a disease that results in a difference in an individual’s appearance like sagging ears or spreading eyes. It can be a part of medical checkups and observe genetic disease.
Final thoughts –
Face recognition applications are the future of this continuously growing and upgrading world.
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