Age Prediction and Image Detection

Python CV2 tkinter Cascade Classifier
RoleSolo Developer
Team Size1
Development Time1 week
Seven faces detected in a selected region, each boxed and labelled with an age bracket

Contributions

Face detection with age bracket prediction Object detection with confidence scores Screen and region capture modes
Live webcam and image file input Colour coding per detected class Running count of what was found

Age Detection
  • Faces get boxed and labelled with an age bracket rather than a single number, since that is what the model actually predicts.
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  • A status line reports how many faces were found, which makes it obvious when the detector misses someone.
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  • Four ways in: an image file, a live webcam feed, the whole screen, or a region dragged out by hand.
Eight faces detected on a captured screen, each labelled with an age bracket
Object Detection
  • A separate image mode labels objects with a class name and a confidence score.
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  • Detection is limited to a defined list of classes, so anything outside it is ignored rather than guessed at.
A motorbike detected at full confidence
Crowded Scenes
  • Each class draws in its own colour, so people and vehicles stay separable when boxes overlap.
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  • Busy frames are where the confidence score earns its place, since it shows which detections to trust.
An airport scene with people and taxis detected in separate colours

Seven faces found inside a hand-drawn region.

Faces detected in a selected region

Eight faces found by capturing the screen directly.

Faces detected from a screen capture

A single object, matched at full confidence.

Motorbike detected

People and vehicles picked out of a busy airport frame.

Multiple objects detected in an airport scene