Technology with time has progressed a lot. Unimaginable things can be seen as real objects. Those small tiles in our phones are a prime example of this. Those applications are there for different kinds of uses. One such part of technology is Deep Learning. Most of the modern-day applications are based on this technology.
Let us first try to know what deep learning is. Deep learning is an artificial intelligence (AI) function that mimics the human brain's processing of data and pattern creation in order to make decisions. It is an artificial intelligence subset of machine learning that uses neural networks to learn unsupervised from unstructured or unlabeled data. Deep neural learning or deep neural network are other terms for the same thing.
Deep learning applications include self-driving cars, virtual assistants, facial recognition, etc. In this blog, we are going to talk about facial recognition using deep learning techniques.
Facial recognition is a method of identifying or verifying a person's identity by looking at their face. People can be identified in photographs, films, or in real-time using facial recognition technology.
This technology is used in various fields nowadays, even many smartphones have this feature where they unlock only when they recognize the face of their owner. Facial recognition comes under biometric security. Voice recognition, fingerprint recognition, and ocular retina or iris identification are all examples of biometric software.
Although the technology is mostly utilized for security and law enforcement, there is growing interest in other applications. The facial recognition technology uses deep learning algorithms to identify and match the face with a database.
Due to advancements in AI, machine learning, and deep learning technologies, the facial recognition business is quickly developing. Facial recognition is a technology that can recognize a person only by looking at them. It uses machine learning techniques to identify, collect, store, and evaluate face characteristics so that they can be matched to photos of people in a database.
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How facial recognition works is fairly a lot to tell. But to understand the concept of it, we need to look at some important problems that a machine needs to solve in order to proceed with it. They are Face detection, face alignment, feature extraction, face recognition, and face verification techniques.
Working Steps for Facial Recognition
To begin, the system must find the face in the image or video. Most cameras now include a built-in facial detection feature. Snapchat, Facebook, and other social media platforms employ face identification to let users apply effects to images and videos taken using their apps. Many apps identify the person in the photo using this, they can even find a person standing in a crowd with this face detection technique.
To a computer, faces turned away from the focal point appear completely different. To normalize the face and make it consistent with the faces in the database, an algorithm is necessary. Using a variety of generic face landmarks is one method to do this.
The bottom of the chin, the top of the nose, the outsides of the eyes, different places surrounding the eyes and lips, and so on are examples. The next stage is to train a deep learning system to locate these spots on any face and turn it towards the center. This makes the face detection process much easier.
(Referred blog: Introduction to Neural Networks in Deep Learning)
This phase entails measuring and extracting numerous characteristics from the face so that the algorithm can compare it to other faces in its database. However, it was initially unclear which traits should be collected and extracted until researchers realized that letting the deep learning system decide which data to gather for itself was the optimal method.
Embedding is a technique that employs deep convolutional neural networks to teach itself to create numerous measurements of a face, allowing it to differentiate it from other faces.
A final deep learning algorithm will compare the measures of each face to known faces in a database, using the unique measurements of each face. The match will be whatever face in your database comes closest to the measurements of the face in question.
Now, in the end, the deep learning algorithms do the final act, which is, matching the face with other faces in the database. If the face matches then it is said to be verified, and if it doesn’t it remains unverified. This step is called face verification. Faces are compared in it to give the final result of a whole long process. But this step is a slightly complex one.
The image can be compared to the database in one of two ways. If the image obtained and the image in the database are both 3-D, the matching procedure will go smoothly. However, because most government offices and other locations use 2-D databases, the comparison becomes more difficult.
Before comparing, the 3-D picture must be transformed into a 2-D image. When compared to a still and stable 2-D image, a 3-D image will be alive and moving. As a result, when a 3-D picture is captured, it is transformed to 2-D by obtaining measurements from distinct places on the face. These measurements will then be translated to an algorithmic form, and therefore a 2-D picture will be created.
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According to its aim, the comparison may also be divided into two categories. Verification is one of them, and identity is the other.
Verification: It is the process of identifying someone who claims to be an employee of a specific office. This sort of database comparison will only be carried out in a 1:1 ratio. That is to say,
Identity: The image obtained will be compared to all of the photos in the database in a 1: N ratio to identify a thief or a perpetrator. Take a look at the graphic below to see how the comparing process works.
In most cases, the comparison is done using three distinct templates. They are;
Vector Template: This template is used to do a fast database search in both 1:1 and 1:N ratios.
LFA (Local Feature Analysis): This template is based on the vector template. This is a more difficult search.
Surface Texture Analysis [STA]: This is the most difficult of the three search templates. It follows the LFA, and the search is focused on the image's skin characteristics, which carry the most information.
When these templates are integrated into face recognition software, the system can detect and identify the individual even when his expressions vary, such as smiling, frowning, or blinking. The software's accuracy is unaffected by the development of a moustache or beard.
Nowadays, facial recognition is used in many industries across the globe. Here we are mentioning the 7 best uses of it.
Face recognition is currently used to unlock a range of phones, including the newest iPhone. This technology is a strong technique to secure personal data and ensure that sensitive data is unavailable to the offender if a phone is stolen.
By making informed estimates regarding people's age and gender, face recognition has the potential to make advertising more targeted. Companies like Tesco are already planning to put displays with built-in facial recognition at petrol stations. It'll only be a matter of time until facial recognition is widely used in advertising. Learn more about how Tesco uses big data analytics.
Face recognition may be used to track missing children and human trafficking victims. As long as missing people are entered into a database, law enforcement can be notified if they are identified by facial recognition in a public place, such as an airport, retail store, or other public areas.
Face recognition applications on mobile phones are already assisting police officers by allowing them to quickly identify people in the field from a safe distance. This can assist them by providing contextual information about who they are working with and whether they should continue with caution.
For example, if a police officer pulls over a wanted killer during a normal traffic stop, the officer will immediately recognize that the suspect is armed and dangerous, and will call for backup.
When Facebook members appear in photographs, Facebook utilizes facial recognition technology to instantly recognize them. This makes it easier for individuals to discover images in which they appear, and it also allows them to recommend when certain persons should be tagged in photographs.
Face recognition can track kids' attendance in addition to making schools safer. In the past, attendance slips allowed students to sign another kid in who was skipping class.
However, many schools already use facial recognition to guarantee that pupils do not skip class. Students' faces are scanned with tablets, and their images are compared to a database to verify their identities.
By automatically detecting persons in surveillance footage or other recordings, facial recognition can help forensic investigations. Face recognition software may also be used at crime scenes to identify people who are dead or asleep.
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Security and surveillance advancements have transformed the way data is collected, as well as how to drive activities and make the greatest use of data in the future. To monitor, identify, and record an incursion, security systems can range from as simple as a video camera to as complicated as a biometric system.
(Related reading: Computer Vision overview that’s redefining surveillance)
Biometric face recognition is gaining center stage in today's surveillance business, which has developed and moved beyond standard cameras. Face recognition is the most effective contactless biometric method thanks to the application of Deep Learning and Artificial Intelligence technologies.
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