image provider

Movie Star Recogniser


Disclaimer

This essay does not describe an existing computer program, just one that should exist. This essay is about a suggested student project in Java programming. This essay gives a rough overview of how it might work. I have no source, object, specifications, file layouts or anything else useful to implementing this project. Everything I have prepared to help you is right here.

This project outline is not like the artificial, tidy little problems you are spoon-fed in school, when all the facts you need are included, nothing extraneous is mentioned, the answer is fully specified, along with hints to nudge you toward a single expected canonical solution. This project is much more like the real world of messy problems where it is up to you to fully the define the end point, or a series of ever more difficult versions of this project and research the information yourself to solve them.

Everything I have to say to help you with this project is written below. I am not prepared to help you implement it; or give you any additional materials. I have too many other projects of my own.

Though I am a programmer by profession, I don’t do people’s homework for them. That just robs them of an education.

You have my full permission to implement this project in any way you please and to keep all the profits from your endeavour.

Please do not email me about this project without reading the disclaimer above.

Let’s say you are watching a movie and you see a familiar face on the screen, but you just can’t quite place it. How can you find the name of that actor, especially if you don’t know the name of the movie?

The proposed program works like this. You enter the name of an actor similar to the mystery actor, then it displays actors similar to that one, e.g. if you entered Brian Dennehy out would pop Charles Durning, complete with photographs of both.

You have a database of photos of actors. You prime it by asking people to rate the similarity of random pairs of actors on a scale from 1 to 10. Instead of testing all possible pairs, you can interpolate. If Ronald Reagan and John Wayne are similar and Bette Middler is ranked as very different from Ronald Reagan, then chances are Bette Middler is also very different from John Wayne. You want to focus your fine tuning efforts on pairs that match similar.

Your scale should more finely discriminate similarity rather than difference.

You can make money with this by setting it up on a webserver dispensing Google ads in the footer.


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