Constant Time Feature Matching for ID Document Type Identification with On-the-Fly Type Subset Selection

Identity document recognition is becoming more and more common in our daily lives. As security measures and document standards improve, the number of documents that need to be recognized is also increasing. So, one of the essential tasks of identity document recognition systems is to identify the document type from thousands of possible variants. However, in many cases, we have supplementary information and can reduce a set of possible types on-the-fly to improve processing speed and quality. In this paper, we discuss ID document recognition with on-the-fly type subset selection. The main challenges in such a system are responding within a limited time and achieving computational and memory efficiency for subset handling. We propose a solution based on a feature-matching approach using binary keypoint descriptors and adjusted multi-index hashing, which uses two new heuristics to ensure a constant number of comparisons for each request. We experimentally evaluate this method on the MIDV-500 and MIDV-2019 datasets and demonstrate that it offers an excellent combination of accuracy, configuration time, and search time compared to commonly used hierarchical clustering, hierarchical navigable small-world graphs, multi-probe locality-sensitive hashing, and straightforward brute-force solutions.