For a given biometric sample, only one feature can be extracted, and only one identity can be assigned
I will describe the issue in terms of face recognition, but similarities can be drawn to other biometrics, for example speaker recognition.
Currently, we assume that in a given image, there is only one identity in an image. When applying face detection, we only take the face with the highest detection score. In an real open-set have datasets as I have used in my latest opes-set recognition challenge (see: http://vast.uccs.edu/Opensetface) , we have several faces per image, and we need to detect all faces (which might end up in an unknown number of misdetections). Finally, we need to extract a feature vector for each detected bounding box, and compare each feature vector to the gallery models.
So far, none of the concepts that we have implemented thus far is dealing with this kind of problem. I am also not so sure if we should implement a solution directly into
bob.bio.base, or if I should try to implement a solution into a stand-alone package.