Fast keypoint filtering for feature-based identity documents classification on complex background

Nargiza Z. Valishina; Alexander V. Gayer; Natalya S. Skoryukina; Vladimir V. Arlazarov

doi:10.1117/12.3023194

3 April 2024 Fast keypoint filtering for feature-based identity documents classification on complex background

Nargiza Z. Valishina, Alexander V. Gayer, Natalya S. Skoryukina, Vladimir V. Arlazarov

Proceedings Volume 13072, Sixteenth International Conference on Machine Vision (ICMV 2023); 1307205 (2024) https://doi.org/10.1117/12.3023194
Event: Sixteenth International Conference on Machine Vision (ICMV 2023), 2023, Yerevan, Armenia

Abstract

The initial steps of many computer vision algorithms are local feature extraction and matching. However, in the problem of recognizing objects in images with complex backgrounds, this approach has a weak point since keypoints may be found not only in the object of interest, but also in the background. This leads to redundant calculations and can cause mismatches. In this paper, we propose a keypoints filtering method applicable to the problem of classification and localization of ID documents in the wild. Using a light-weight deep learning model, keypoints are divided into ”document” and ”background” classes, after which the keypoints of the background are removed. Experimental results show that adding the proposed filtering step gives an average speedup of 3.14% on the entire MIDV-500 dataset and 14.77% on MIDV-2020. At the same time, the acceleration on target images with complex backgrounds reaches 81%.

Citation Download Citation

Nargiza Z. Valishina, Alexander V. Gayer, Natalya S. Skoryukina, and Vladimir V. Arlazarov "Fast keypoint filtering for feature-based identity documents classification on complex background", Proc. SPIE 13072, Sixteenth International Conference on Machine Vision (ICMV 2023), 1307205 (3 April 2024); https://doi.org/10.1117/12.3023194

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