AI Face Recognition

Addressing the Frequently Asked Questions about AI Face Recognition

AI face recognition refers to the use of artificial intelligence for enhancing biometric facial recognition systems. These systems have long been used for enhancing cybersecurity measures within businesses and are also employed as an access control method. By the end of this blog, you will gain a clear understanding of how AI face recognition works and the industries that use this technology.

Understanding Facial Recognition Systems

A facial recognition system is a technology that was built for distinguishing and identifying individuals from an image or video. These systems have been around for quite some time. However, its adoption has become more prominent, and accessible, within the last few years. This is because the use of AI face recognition is no longer limited to just law enforcement agencies. In fact, sectors such as healthcare, finance and banking, and education are increasingly utilizing this technology for enhancing security. To understand its capabilities, we must first understand how facial identification works.

AI face recognition allows its users to analyze the facial features found in a video or an image. For instance, this technology can measure the distance between two eyes, shape of the chin, the contour of the lips, the width of the forehead, etc. to identify a person. These detected facial features are then used by businesses that need to analyze millions of individuals within seconds.

The Role of ML and AI in Face Recognition

Every technology that is based on machine learning (ML) and AI algorithms takes an input of datasets, learns from this data, and provides an output based on it. This is done by analyzing various patterns found in the data and producing results according to them. For example, to identify an individual through facial characteristics, the following attributes are analyzed to form patterns:

  • Width of the face
  • Height to width ratio of the face
  • Complexion
  • Distance from one ear to the other
  • Length/width of the nose, chin, forehead, etc.

Once these attributes have been analyzed, a numerical representation of the face is made. This is known as a feature vector. In the simplest words, AI and ML perform the following three tasks to enable facial recognition online:

  • Determine the feature vector
  • Find and analyze algorithms
  • Face recognition operations
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Understanding the Face Recognition Operations

Each AI face recognition software functions differently from another. However, the basic steps remain common. These include:

1.     Face Detection

In the first step, a CCTV camera or computer webcam captures the image of a person to detect their face. This works the best when the person looks directly into the camera. However, the latest technological advancements in AI facial recognition have bypassed these limitations, such that even if an individual’s face is slightly moved, the image can be captured.

2.     Facial Analysis

Once the image has been captured, the next step is to analyze it. In this stage, facial attributes such as the shape of the eye or cheekbones are analyzed and patterns are formed.

3.     Image to Data

After the analysis of facial features, the image is converted into a numerical form. This mathematical form is known as a face print. Similar to how every individual has distinct fingerprints, face prints are also unique to every individual.

4.     Finding a Match

The numerical data is compared against a database of biometric face prints. This database has known photos against which the information can be accurately matched. Once a match has been detected, supporting information also becomes available. This can include a person’s name, address, date of birth, nationality, and much more.

Industries That Require AI Face Recognition

1.     Finance and Banking

AI face recognition systems are commonly deployed by financial institutions such as banks, investment companies, insurance firms, etc. Face identification systems are particularly essential for secure customer onboarding. This is because fraudsters commonly target financial institutions to commit fraud. For accurate detection of these fraudsters, biometric technology plays a major role.

2.     Healthcare

Healthcare institutions also require the use of AI face recognition for accurate patient identification. Imposters and identity thieves often steal patient data through company data breaches and use it to create a new identity. This way, they are able to gain illegal access to medical benefits, surgical treatments, and even manipulate drug manipulation.

3.     Education

Educational institutions have also joined the world of biometric technology. Today, AI face recognition is commonly used as a streamlined system for marking student attendance. Additionally, biometric facial recognition is also being used to keep a check on a student’s level of attentiveness and for conducting exams online. Given the current state of the pandemic, this technology has significantly enhanced e-learning platforms by providing them a secure mechanism of imparting knowledge to the generation.