Do you know that there exist 2200 cyberattacks every single day? This means that people around the globe come across a new cyberattack every 39 seconds! 

Technology is advancing, so are the hackers. Cybercriminals are acquiring more sophisticated tactics to have unauthorized access to the network or to carry out illicit activities. As everything is prone to digitization, internet crimes are progressing. According to the internet crime report 2020, FTC received 791,790 internet crime complaints. The top 3 crimes that were reported by victims in 2020 were extortion, phishing scams, and non-delivery/non-payment scams. There is not a single sector left on this earth that has not faced any sort of cyber attack. 

Will this world let these cybercrimes evolve? Are tech giants and financial infrastructures acquiring appropriate measures to combat these attacks? 

Well, with great power comes great responsibility, and it’s the major responsibility of sectors to protect their customer’s data by incorporating real-time, reliable, and robust identity verification services and solutions. Having said that, utilization of innovative technology such as machine learning as well as artificial intelligence adds up to these identity verification solutions, especially face recognition systems.  

But what do machine learning and facial recognition actually are? And how machine learning revolutionizes face verification solutions? Let’s plunge in!

What is Machine Learning? 

I’m sure that you come across a trillion of wordy definitions of machine learning when you google it, but here let me explain this term in layman terms. 

A study of computer algorithms that are automatically enhanced through experience and by the utilization of an immense volume of data is called machine learning. Machine learning algorithms develop a model on the basis of data which is known as training data to make decisions and predictions.

Machine learning is the segment of a very vast branch i.e. artificial intelligence. Machine learning algorithms permit machines to learn from their past experience, analyze the output and utilize it for the input of their next operation. Machine learning algorithms are used to solve complex problems with conventional programming. The branch of machine learning is deep learning. 

Some common applications of machine learning are as follow: 

  • Traffic prediction
  • Virtual personal assistant 
  • Self-driving cars
  • Email spam and malware filtering 
  • Online fraud detection 
  • Product recommendation 

What is Facial Recognition? 

As simple as the words explain themselves, facial recognition refers to the latest and innovative technology to ensure the living presence of the customer. This technology is capable of identifying a person on the basis of his facial features. Face identification technology is grounded with complicated mathematical machine learning and artificial intelligence algorithms. 

Facial recognition system capture, store, and analyze facial features of an individual and match them with the image in a pre-existing database. Facial recognition is the subset of biometric identification. Biometric identification not only includes analysis of facial features but also includes voice recognition, signature recognition, recognition of fingerprint, iris, palm, and gait. 

Some common applications of facial recognition are as follow: 

  • Aid forensic investors 
  • Mobile phone unlock 
  • Recognizes VIP at sporting events 
  • Facilitate secure transactions 
  • Track attendance 
  • Control access to sensitive areas 

How Machine Learning is Utilized in Facial Recognition Technology? 

Facial recognition technology is maturing at a swift pace due to the advancements in artificial intelligence and its subsets together with machine learning and deep learning algorithms. Face recognition technology analyzes unique facial features of individuals to differentiate among them. Here’s how facial recognition technology utilizes innovative machine learning algorithms to make it more robust and reliable. 

  • Face verification: It involves the comparison of unique facial characteristics. Machine learning algorithms are used to analyze whether the face matches with the face picture pre-stored in the database or not. 
  • Face recognition: The unique measurements of each face of every individual are matched with the face priorly stored in the database for user authentication. 
  • Facial feature measurement and extraction: Facial features are analyzed and extracted using innovative machine learning algorithms which help in decision making. This procedure is called embedding and it utilizes convolutional neural networks to distinguish a face from numerous other faces. 
  • Face alignment: Faces appear differently to the computer systems. Multiple generic facial landmarks are utilized to normalize face consistency with the face images that are priorly stored in the database. In the next step, machine learning algorithms are trained to find facial features and centralize them. 
  • Face detection: The face of an individual is detected and distinguished on an image or a video. Cameras are getting so innovative that they have built-in face detection functions. Face detection is also utilized by numerous social media platforms such as Snapchat, Facebook, Instagram, etc. 

Step by Step Guide 

Numerous machine learning algorithms are chained in a facial recognition system. Let’s have a look at the step by step guide on how these innovative machine learning algorithms make facial recognition system more robust: 

  1. Pictures are encoded using the HOG algorithm for creating a simplified version of the picture. After that, find that part of the face from a simplified picture that mostly looks exactly like a generic HOG face’s encoding. 
  1. Distinguish the posture of the face by figuring out the main landmarks on the face. Use those landmarks to wrap the picture to place eyes and mouth at the center. 
  1. Pass that centered face picture through a neural network that is capable of measuring facial features appropriately. After that, save those 128 measurements. 
  2. Match the person’s picture with the pictures which are stored in the database to find the closest match. 


Enterprises of all sizes come across different sorts of people each day and it’s a time for them to incorporate such measures that can effortlessly differentiate between their potential customers and cybercriminals. Technological advancements and hackers are growing in parallel to each other. Fraudsters are getting so advanced that they break into the network in a matter of seconds. Machine learning-powered facial recognition systems play a promising role in the detection and prevention of cybercriminals. 

Consequently, from government sectors to private sectors, from financial infrastructures to travel industry, from educational sectors to food industry, each and every single sector must not even think twice to integrate themselves with such solutions that provide reliable face identification services. Hence every sector must enhance its cybersecurity protocols with the incorporation of robust identity verification mechanisms.