recognition and detection are two different things - for detection deep networks are performing as state of the art. Recent for recognition they are also doing well. However, one main difference is that many recognition tasks traditionally have very few images per object/user (especially when you're testing over hundreds or thousands of subjects). DeepFace by facebook showed that deep nets can be useful in recognition but set a rather unreal (at the moment) president that 100s-1000s of images can be used per subject.

So what's the difference between recognition and detection?