Reference to the Andrew Deep Learning Course. It was told by instructor that the recognition system is more challenging than a verification system so following is the transcript of the video I pasted below
"So, the recognition problem is much harder than the verification problem. To see why, let's say, you have a verification system that's 99 percent accurate. So, 99 percent might not be too bad but now suppose that K is equal to 100 in a recognition system. If you apply this system to a recognition task with a 100 people in your database, you now have a hundred times of chance of making a mistake and if the chance of making mistakes on each person is just one percent. So, if you have a database of a 100 persons and if you want an acceptable recognition error, you might actually need a verification system with maybe 99.9 or even higher accuracy before you can run it on a database of 100 persons that have a high chance and still have a high chance of getting incorrect. In fact, if you have a database of 100 persons currently just be even quite a bit higher than 99 percent for that to work well. "
I did not get as to what the instructor was trying to say? Can somebody explain that with some simple example?