With time, technology has made great strides. Unthinkable things can appear to be actual objects. The little tiles on our phones are a good illustration of this. There are numerous uses for those applications. Deep Learning is one of these technological components. The majority of contemporary applications are built using this technology.
Deep learning is a feature of artificial intelligence (AI) that imitates how the human brain processes data and builds patterns to form judgments. It is a branch of machine learning that applies artificial intelligence to learn unsupervised from unlabeled or unstructured data. Other names for this concept are deep neural networks or deep neural learning.
About Facial Recognition
A technique for recognising or confirming a person's identification by glancing at their face is facial recognition. Facial recognition technology can be used to identify people in real-time, on-screen, or in photos and movies.
These days, technology is applied in many other industries; many mobile phones even have the capability to only unlock when their owner's face is recognised. A biometric security measure is facial recognition. Examples of biometric software include voice recognition, fingerprint recognition, and iris or retina identification in the eye.
Despite the fact that the technology is mostly used for security and law enforcement, interest in other applications is growing. Deep learning methods are used by facial recognition software to identify and match a face to a database.
Steps involved in Facial Recognition Systems
The facial recognition industry is expanding swiftly thanks to developments in AI, machine learning, and deep learning. A person can be recognised by facial recognition technology just by glancing at them. It identifies, gathers, stores, and evaluates facial attributes using machine learning algorithms so that they may be compared to images of people in a database.
The algorithm first needs to identify the face in the picture or video. A facial detection feature is currently included into the majority of cameras. Face identification is a technique used by Snapchat, Facebook, and other social media sites to allow users to add effects to photos and videos captured with their apps. This face detection method is used by several apps to identify the person in the picture; they can even locate a person standing among a huge crowd.
Faces turned away from the main point appear quite different to a computer. An algorithm is required to normalise the face and make it consistent with the faces in the database. One way to do this is by using a range of generic face landmarks.
Examples include the outsides of the eyes, the top and bottom of the nose, various areas around the lips and eyes, and so on. The last step is to train a deep learning system to identify these areas on each face and rotate it so that it faces the centre. This greatly simplifies the process of face detection.
- Face Measurement and Extraction
In order for the algorithm to compare the face to other faces in its database, this phase involves measuring and extracting a variety of attributes from the face. But before researchers learned that allowing the deep learning system choose which data to gather for itself was the best approach, it was unclear which traits should be gathered and extracted.
Using the particular measurements of each face, a final deep learning algorithm will compare the measurements of each face to known faces in a database. The closest match to the face's measurements in your database will be used as the match.
The deep learning algorithms now complete the last step, which involves comparing the face with other faces in the database. If the faces match, the claim is considered verified; otherwise, it is considered unverified. Face verification is the term for this action. It compares faces to produce the end product of a protracted procedure. However, this stage is a little challenging.
One of two methods can be used to compare the image to the database. The matching process will proceed smoothly if the obtained image and the image in the database are both three-dimensional. However, the comparison gets more challenging because most government agencies and other places employ 2-D datasets.
The 3-D image must be converted into a 2-D image before comparison. A 3-D image will appear to be alive and moving when compared to a static and steady 2-D image. Because of this, when a 3-D image is taken, it is converted to a 2-D image by taking measurements from various points on the face. These measurements will then be converted to an algorithmic form, which results in the creation of a 2-D image.
Uses of Face Recognition Systems
Numerous phones, including the most recent iPhone, may now be unlocked using face recognition. With the help of this technology, it is possible to protect critical information and make sure that if a phone is stolen, the thief cannot access personal information.
The ability to swiftly identify persons in the field from a safe distance is already helping police officers thanks to face recognition programmes on mobile devices. This can help them by giving them perspective about the people they are working with and whether they should proceed cautiously.
For instance, if a policeman stops a wanted murderer during a routine traffic check, the officer will immediately realise that the man or woman is armed and dangerous and will request assistance.
- Identifying People on Social Media
Facebook uses face recognition technology to automatically identify people who appear in pictures. This makes it simpler for people to find pictures in which they feature and gives them the ability to suggest when certain people should be tagged in pictures.
The use of facial recognition in surveillance footage and other recordings can aid forensic investigations by automatically identifying people in the footage. To identify people who are already dead or asleep, face recognition software may also be utilised at crime scenes.
Conclusion
The method that data is acquired, as well as how to direct operations and make the most use of data going forward, has changed as a result of security and surveillance improvements. Security systems can be as straightforward as a video camera or as complex as a biometric system in order to track, recognise, and record an intrusion.
As surveillance technology has advanced and gone beyond simple cameras, biometric facial recognition is taking centre stage. Face recognition is the most reliable contactless biometric technique today because of the integration of Deep Learning and AI technology.
References
- https://www.freepressjournal.in/sponsored-content/how-data-science-is-helpful-in-advanced-image-recognition
- https://www.analyticssteps.com/blogs/how-does-facial-recognition-work-deep-learning
- https://www.datatrained.com/dt-gyan/show-videos/128
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