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Step 2: Image Processing: The computer breaks the image into tiny dots called pixels. Then it
uses algorithms (smart instructions) to detect patterns and features in the image.

Step 3: Object Recognition: The system tries to identify what’s in the image—like a face,

object, shape, or even text—by comparing it with what it has learned before (pre trained
data).

Step 4: Decision-making: Based on what it sees, the computer takes action. This could mean
moving a robot, raising an alarm, stopping a machine, or making an independent
decision. For example,

At an international airport, a tech company is developing a facial recognition system to

enhance passenger security and speed up the check-in process. The system is built in
three main stages.

Training Analysis Interpretation

System analysis new System analysis new System interpretation
images, identifying key images, it identifies analysis data and makes
features and patterns key patterns a decision

In the first stage, the developers upload a large number of face images into the system.

These images include people of different ages, backgrounds, and expressions. This
helps the system learn the key features of a human face, such as eye position, nose
shape, and facial structure. This stage is known as training, where the system learns
from a large dataset to recognise different types of faces.

In the second stage, the system begins to analyse the new images it receives. It studies

important patterns and compares them with what it learned during training. It looks for
specific details that make each face unique, such as the distance between eyes or the
shape of the jawline. This stage is called analysis, where the system identifies useful
features from the input data.

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