The state of the art says that we can use PCA to perform face recognition.
like this, this or this
I am working with a project that involves training a classifier to detect a person who is wearing glasess or hats or even a mustache.
The purpose of doing this is to detect when a person that has robbed a bank, store, or have commeted some sort of crime(s) (we have their image in a database), enters a certain place ( historically we know these guys have robbed, so we should take care to avoid problems).
We came first to have a distributed database with all images of criminals, then
I thought to have a layer of them clasifying these criminals using accesories like hats, mustache or anything that hides their face etc... Then, to apply that knowledge to detect when a particular or a suspect person enters a comercial place. ( In practice when someone is going to rob not all the times they are using an accesorie...)
What do you think about this idea of doing PCA to first detect principal components of the face and then the components of an accesory.
I was thinking that maybe a probabilistic approach is better so we can compute the probability the criminal is the person that entered a place and call the respective authorities.
