Memory consumption reduction for identity document classification with local and global features combination

Natalya Skoryukina; Vladimir V. Arlazarov; Artemiy Milovzorov

doi:10.1117/12.2587033

4 January 2021 Memory consumption reduction for identity document classification with local and global features combination

Natalya Skoryukina, Vladimir V. Arlazarov, Artemiy Milovzorov

Author Affiliations +

Proceedings Volume 11605, Thirteenth International Conference on Machine Vision; 116051G (2021) https://doi.org/10.1117/12.2587033
Event: Thirteenth International Conference on Machine Vision, 2020, Rome, Italy

Abstract

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%.

Citation Download Citation

Natalya Skoryukina, Vladimir V. Arlazarov, and Artemiy Milovzorov "Memory consumption reduction for identity document classification with local and global features combination", Proc. SPIE 11605, Thirteenth International Conference on Machine Vision, 116051G (4 January 2021); https://doi.org/10.1117/12.2587033

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available