Technology has evolved from being a problem-solving force to a purpose-driven entity for humans. Looking back at the 1990s, technology was limited to computers, the internet, emails, wired telephony, but advancements in technology have led to a sweeping change in its role as an indispensable necessity in our lives and society. One of the key advances is the advent of artificial intelligence (AI) and machine learning (ML), which were conceptualized to replace human intervention for mundane tasks or even handle mission-critical applications and contribute to safeguarding. In the subsequent sections, we will see the expanse of AI and ML in various use cases as well as understand their role in one of the most advanced forms of biometric security — facial recognition.
What Is Artificial Intelligence (AI)?
AI is a technology that simulates human intelligence processes using machines to make cognitive decisions. From recognizing faces on your mobile lock screen to self-driving cars, AI has unleashed a new realm of leveraging technology.
How Does AI Work?
AI uses a set of unstructured data to analyse information patterns using AI algorithms and correlate the information to provide outcomes. Being programmed to make cognitive decisions, AI augments various forms of automation by harnessing neural networks, machine learning, and deep learning to arrive at a decision.
Where Is AI Used?
Artificial Intelligence is an integral part of various applications and SAS software. Some of the popularly used applications include image recognition, speech recognition, natural language generation, sentiment analysis, and chatbots to name a few.
AI offers a slew of characteristics that look promising for the products of tomorrow. AI can automate frequent, high-volume tasks by learning and discovering through data. Secondly, it adds intelligence to products in the areas of automation, conversational platforms, smart machines, and bots. Thirdly, it is capable of self-learning through algorithms by finding structure and regularities. Next, AI performs a deep dive of the data sets to facilitate building complex products like fraud detection systems. Further, AI uses deep learning to ensure incredible accuracy by progressively analyzing the inputs. Lastly, Artificial Intelligence helps in monetizing data for businesses to stay ahead of the curve.
What Is Machine Learning (Ml)?
Machine Learning is a subset of AI that mainly focuses on using data and algorithms to mimic human learning. It uses statistical methods to train algorithms to classify or predict and even provide insights into data mining projects. Terms like deep learning and machine learning and sometimes neural networks are generally interchangeably used in the industry. However, there are subtle differences between these technologies. A neural network is a subset of deep learning while deep learning is one of the arms of machine learning.
Simply put, deep learning involves training algorithms with minimal human intervention. It converts unstructured data to manageable groups for processing through a process known as dimensionality reduction.
On the other hand, neural networks also known as artificial neural networks comprise node layers – an input layer, multiple hidden layers, and an output layer. Each of the nodes has an associated weight and threshold and is connected to the other nodes. Basically, if the value of any output layer exceeds its threshold, data is sent to the next layer of the network. Neural networks are of two types: basic neural networks and deep neural networks. In the basic neural network, two or three layers are present whereas a deep neural network consists of more than three layers.
How Does ML Work?
An ML algorithm has three components:
- Decision process
- Error Function
- Model Optimization
In the decision process, an initial input is analysed to make a prediction or estimation of the pattern in the data. In the second phase, the prediction is evaluated based on existing examples. In the model optimization phase, discrepancies between the model estimates and known examples are reduced and optimize the weights until a preset accuracy threshold has been achieved.
Where Is ML Used?
ML is widely used for automatic speech recognition using natural language processing to convert human speech into text. Moreover, speech recognition is used in mobile devices to facilitate voice search. Secondly, it is gaining popularity as a touchpoint for customers on websites and apps by answering frequently asked questions around pricing, shipping, delivery, feedback, and returns. Moreover, ML is being used in virtual assistants and voice assistants to perform mundane tasks.
ML is used in computer vision to derive meaningful insights from images, videos, and visual inputs, especially in the field of radiology and self-driving cars. In marketing, ML is used for analyzing consumer behaviour patterns to devise cross-selling strategies and give recommendations to consumers on e-commerce websites.
What Is Facial Recognition?
Facial recognition is one of the front-runner applications of AI. It is one of the advanced forms of biometric authentication capable of identifying and verifying a person using facial features in an image or video from a database.
Why Is Facial Recognition So Important Now?
In recent years, there have been increased investments in facial recognition technology. Venture funding in facial recognition start-ups has seen a huge uptick in 2021. With advancements in this technology, new use cases and business models in the field of advertising, healthcare, security, proctoring, airports, etc.
How Does Facial Recognition Work?
Face recognition uses AI algorithms and ML to detect human faces from the background. The algorithm typically starts by searching for human eyes, followed by eyebrows, nose, mouth, nostrils, and iris. Once all the facial features are captured, additional validations using large datasets containing both positive and negative images confirm that it is a human face. Some of the common techniques used for facial recognition are feature-based, appearance-based, knowledge-based, and template matching. Each of these methods has its advantages and disadvantages.
Feature-based methods rely on features such as eyes or nose to detect a face. The outcomes of this method could vary based on noise and light. Further, appearance-based methods use statistical analysis and machine learning to match the characteristics of face images.
In a knowledge-based approach, a face is recognized based on predefined rules. This could be challenging considering the efforts needed to define well-defined rules. Whereas template-matching methods compare images with previously stored face patterns or features and correlate the results to detect a face. However, this method fails to address variations in scale, pose, and shape.
Artificial Intelligence and machine learning offer a multitude of opportunities and endless possibilities to work for the betterment of the world. However, it is essential to pay attention to the ethics and privacy of people while dealing with data. Data storage, management, and security are the other aspects that will play an important role in making these technologies invasive.