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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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