I have a face dataset on which I am using Azure Face Service for identification of people. The first step is to detect faces using face.detect in data then enroll if face is present using face lists. The second step is to train the enrolled set. The third step is getting inference by detecting faces on the same dataset using face.detect and then finding out what faces are there in the database that matches with it using find similar method.(Eg : Images 1,2,3 are enrolled; 1,2 is a match;1,3 is a match;2,1 is a match;3,1 is a match so on...).
Now, the problem is for large number of images the api calls would become way more expensive and we can see that the first and third step both uses face detect api call and returns a face id with its other properties that expires in 24 hours. Is there any way to minimize the api calls in this scenerio (Enrolling entire dataset and then verifying from the enrolled dataset itself).