Memory consumption reduction for identity document classification with local and global features combination
Natalya S. Skoryukina, Vladimir V. Arlazarov, Artemiy Milovzorov · 2021
In this paper we explore possibilities of memory cost reduction without significant loss of classification accuracy in connection with the problem of the ID document type recognition on mobile devices. The studied classic approach is based on representing images using constellation of feature points and descriptors. The distortion parameters are estimated by applying RANSAC. Experimental data details the approach limitations (memory, speed and accuracy) in dependence of the descriptor type. In order to maintain accuracy when using low dimensional descriptors we suggest to modify the basic approach using additional features characteristic of the document such as straight lines and quadrangles. In addition, an early filtration of the samples and the hypotheses used in RANSAC. It was shown that the proposed modifications have a positive contribution for all types of descriptors considered. The suggested algorithm was tested using the open dataset MIDV-500. The modified approach allows to achieve an accuracy improvement and significant speed up of distortion parameters estimation in RANSAC. It was shown that using compact descriptors in conjunction with the presented method allows reduce required memory cost by more than 7 times with near-zero (0.2%) loss of accuracy, and more than 14 times with the loss of accuracy is about 18%.