What is the use of perception calculation in Speaker Identification?
Well, I don't know whether it'll actually help you – you just said it would! 

Now, in any case, using an algorithm to extract features from a signal that mimics or resembles human perception should inherently give those feature vectors a higher "mathematical" resemblance when a human would find the original signals similar, too.

This is obviously what you want in speech recognition – after all, the computer is supposed to do something that is very human.

Now, the question is whether that helps in identifying speakers. 

In my first, intuitive, reaction to that, I'd say, no, applying what is used for speech recognition to speaker identification might be a bad idea: A good speech recognizer should be as invariant as possible to the speaker – and thus, "lose" the information of who spoke somewhere on the way.

In reality, something less than that extreme is true: You still want to analyze speech (as opposed, to let's say, interpret engine sounds) – but under a different aspect. So, using the same  basic steps to extract feature vectors sounds pretty clever. You might want to tweak parameters; for example, though not overly important for speech recognition, higher-frequency sound might be helpful in telling high-pitched female teens from smokey-voiced old men.