Does this technology work using both images simultaneously? Or could lots of images be pre-processed individually into something that allowed a cheaper comparison?
Just to try to explain the question better, here's an example. Let's say I have 10 images and I want to find the most similar people among any pair of images. Do I need to run every pair of images (45 full comparisons) or can I pre-process the 10 images into something such that the 45 comparisons can be done in a less expensive way?
Recognition is generally broken down into two steps, processing faces into "templates" and then comparing those templates. Generating templates includes all the preprocessing stuff as well: detecting faces in an image, estimating their pose, and finding landmark points. Our site goes into these issues in some depth (with some examples). So yes, we do break the process down: generating the templates can be done individually for every image, which allows you store that result and use it for future comparisons.
Generating 2 templates is many more times expensive than comparing those templates. However, as your dataset grows, generating templates grows at N, and the number of comparisons you need to do grows at N^2. So eventually, comparisons dominate.
Just to try to explain the question better, here's an example. Let's say I have 10 images and I want to find the most similar people among any pair of images. Do I need to run every pair of images (45 full comparisons) or can I pre-process the 10 images into something such that the 45 comparisons can be done in a less expensive way?