Image Annotation For Facial Recognition And How It Works?
Glance
You've probably used your face to unlock your phone at some point. All you have to do is simply look at your phone's camera to unlock your phone with your face, and boom! The phone has been unlocked. Your face is the new fingerprint. Have you seen the Facebook feature where, whenever someone posts a picture of you, Facebook recognizes your face automatically? Facial recognition can be seen in all of these situations. Humans, like programmes, can recognize the faces of their friends, family, acquaintances, and other people. But we are not as fast and accurate as computers.
However, it's fascinating to learn how facial recognition works. It begins with the image Dataset For Machine Learning model. The model is developed, trained, tested and validated to recognize different people’s faces accurately. In this post, we will learn what facial recognition is, how it works, its applications, real-life examples, and more.
What is Facial Recognition?
Facial recognition is the ability of a computer/program to detect and identify a face. Facial recognition technology maps facial features and uses stored faceprint data to help identify a person. This biometric technology compares the stored face print with the live image using deep learning algorithms. To find a match, face detection software compares captured images to a database of images. Facial recognition is a form of biometric recognition. Voice recognition, fingerprint recognition, and eye retina or iris recognition are all examples of biometric software. Although the technology is primarily used for security and law enforcement, there is growing interest in other industries too.
How does Facial Recognition work?
Facial recognition technology is well-known to many of us, thanks to Face ID, which is used to unlock iPhones (however, this is only one application of face recognition). Typically, facial recognition does not use a large database of photos to determine an individual identity, instead, it simply identifies and recognizes one person as the device’s sole owner, while restricting access to others.
Computer vision is used to collect facial recognition dataset and process images in facial recognition software. The images are digitally screened to ensure that the computer can tell the difference between a human face, a picture, a statue, or even a poster. Patterns and similarities in the dataset are identified using machine learning that are further processed for Image Annotation. The ML algorithm recognizes facial feature patterns to identify the face in any image, like:
The face’s height to width proportion
The complexion of the person
The width of each feature, such as eyes, nose and mouth
Unique characteristics
Facial recognition software, like different faces, has different features. In general, any facial recognition system follows the following procedure:
1. Face detection: Whether a person is in a crowd or not, facial technology systems recognize and identify a facial image. Advances in technology have made it easier for the software to detect facial images even when there is a slight difference in posture- facing the camera or looking away.
2. Face Analysis: The captured image will then be analyzed from the image datasets. A face recognition system is used to accurately identify unique facial features such as eye distance, nose length, mouth to nose distance, forehead width, brow shape, and other biometrical attributes.
3. Image conversion: Following the capture of a face image, the analogue data is converted into digital data based on the person’s biometric features. Because machine learning algorithms can only recognize numbers, it’s necessary to convert the facial map into a mathematical formula. The faceprint or numerical representation of the face is then compared to a database of faces.
4. Finding a match: Finally, your face is compared to several databases of known faces. The software tries to match your characteristics to those in the database. The person’s name and address are usually returned with the matched image. If this information is not available, the saved data is used.
What are the applications of facial recognition?
Facial recognition technology has a variety of applications/use cases, some of them are:
1. Unlocking Phones: Facial recognition is used to unlock a variety of phones, including iPhones. The technology provides a powerful way to protect personal information and ensures that sensitive data is inaccessible even if the phone is stolen.
2. Airports: Biometric passports are becoming increasingly popular among travelers, as they allow them to bypass the usual long lines and instead walk through an automated ePassport control to get to the gate faster.
3. Retail: There are many ways facial recognition can help the retail industry. When known shoplifters, organized retail criminals, or people with a history of fraud enter stores, facial recognition is used to identify them. Customers’ shopping experience can also be improved with this technology. Offline stores, for example, could recognize customers, make product recommendations based on their purchase history, and direct them to the appropriate location.
4. Banking: Another advantage of face recognition is biometric online banking. Customers can authorize transactions by looking at their smartphones or computer instead of using OTP.
5. Marketing and advertising: Consumer facial data, such as different facial expressions, can be used by brands to target targeted ads.
6. Healthcare: Facial recognition is used in hospitals to assist with patient care. Facial recognition is being tested by healthcare providers to gain access to patient records, streamline registration, detect emotion and pain in patients, and even help identify specific genetic diseases through medical dataset.
7. Tracking attendance: Employers and Schools can use this technology to track the attendance of their workers and students.
Real-Life Examples of Facial recognition
Face recognition is used by Apple to help users unlock their phones, log in to apps, and make purchases quickly.
Face recognition has been used by Coca-cola in a variety of ways around the world. Customers are rewarded for recycling at some of its vending machines in china, personalized ads were delivered on its vending machine in Australia and event marketing in Israel.
In 2010, Facebook began using facial recognition in the US when it used its tag suggestions tool to automatically tag people in photos.
How GTS helps with Image Annotation?
You must train the facial recognition model on a variety of heterogeneous datasets in order for it to perform at its best. Because facial biometrics differ from one person to another, the software must be capable of reading, identifying, and recognizing any face. We at Global Technology Solutions create various other datasets like Audio Dataset, Text Datasets, Video Dataset with data Annotation services and Audio Transcription services. That’s why we at Global Technology Solutions (GTS) provide the highest quality datasets that will be used to train, test and validate your machine learning model.