I would like to ask, what are appropriate ways to adjust thresholds for a biometric system when we have only training and testing data of a single user? I am using Speaker, face and gait recognition but in mobile device scenario. In mobile scenario we know that we have always 1:1 matching case (We can go for 1:n matching but this would make system less usable as it is time taking, and need bigger data sets and we don't need it.). So, in this situation I want to find some way where I can find threshold from the training data. and whenever i get some test data I assume that this data is coming from genuine user and after matching, threshold basically decided if a that person is genuine/impostor.
It depends on how the learning is done by the biometrics system.
If the system is distributed to you with prior knowledge of what a human face is, and if you're only teaching it the faces it should accept, it would be enough to just enrol the authorised users.
If the system is merely a computer vision learning model without any prior knowledge, you'd need to build a dataset of human faces representative of the human population, label the faces of the authorised users as authorised and the others as denied, and train the system.
I've never used the system you're asking about, so if you cannot figure out in which of those two categories it falls, I suggest you contact the customer support of the manufacturer.
Without training the system with data from other individuals, the concept of threshold is largely pointless.
When learning, all biometric systems need to be adjusted for false reject rate and false accept rate (FRR and FAR), but in your situation training will not give you any FAR data.
So although you can very easily make sure your system will always recognise your valid user, you have no idea whether it will accept no others, some who look like them or all humans (or possibly dogs, ostriches...who knows)
You need to be able to allow the system to determine the differences between your valid user and non-valid users, and then work out whether you can cope with some FRR (your user is carrying something heavy, perhaps) or with some FAR (someone with very similar gait and matching beard, say)
In summary - you can't do this unless you get a data set from many individuals