COMPUTING SYSTEMS AND NETWORKS
DATA PROCESSING AND ANALYSIS
INTELLIGENCE SYSTEMS AND TECHNOLOGIES
D. V. Polevoy, E. I. Panfilova,, D. P. Nikolaev White Balance Correction for Detection of Holograms in Color Images of Black and White Photographs
MATHEMATICAL FOUNDATIONS OF INFORMATION TECHNOLOGY
D. V. Polevoy, E. I. Panfilova,, D. P. Nikolaev White Balance Correction for Detection of Holograms in Color Images of Black and White Photographs
Abstract. 

Taking a photo or video of a user's identification document is one of the ways to comply with the law when you use mobile services. It is designed to reduce the number of illegal actions. An element of protection against malefactor attacks on providing the original document is the detection and recognition of optically variable document security elements. The key feature of optically variable elements is the appearance and disappearance of high saturation colors during shooting under different angles. The accuracy of color saturation measurement algorithms is limited by the automatic white balance correction subsystem, which uncontrollably changes the color characteristics of the image from frame to frame. In this paper, we considered the possibility of using prior information about color consistency of the document area to compensate distortion of the white balance on the examples of document owner monochrome photographs. The proposed method of color correction increases the contrast of color saturation between the unprotected areas of the photograph and the areas covered by protective film with optically variable devices. The method is tested on a real data obtained with a mobile device.

Keywords: 

white balance, digital image color correction, optically variable device detection.

PP. 82-95.

DOI 10.14357/20718632210308
 
References

1. Arlazarov V. V. and others. Sistema dostupa k distancionnomu polucheniyu bankovskih uslug [System of access to remote receipt of banking services] // Patent RF. №161478. 2016
2. Cook S. Selfie banking: is it a reality? // Biometric technology today. 2017. №. 3. Pp. 9-11.
3. Bulatov K.B. and Ivanov A.A. Programma biometricheskoj identifikacii na mobil'nom ustrojstve po udostoveryayushchej fotografii. [A program for biometric identification on a mobile device using an authenticating photo] // Patent RF № 2019665172. 2019
4. Polevoy D. V. Ispol'zovanie mobil'nyh ustrojstv dlya vyyavleniya priznakov fabrikacii dokumentov, udostoveryayushchih lichnost' [Using mobile devices to detect signs of identity documents counterfeit] // Sensornye sistemy [Sensory systems]. 2019. V. 33. №. 2. Pp. 142-156.
5. Bulatov K. et al. Smart IDReader: Document recognition in video stream // 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2017. V. 6. Pp. 39-44.
6. Chernov T. S., Nikolaev D. P., Kliatskine V. M. A method of periodic pattern localization on document images // Eighth International Conference on Machine Vision (ICMV 2015). International Society for Optics and Photonics, 2015. V. 9875. P. 987504.
7. Chernov T. S., Kolmakov S. I., Nikolaev D. P. An algorithm for detection and phase estimation of protective elements periodic lattice on document image // Pattern Recognition and Image Analysis. 2017. V. 27. №. 1. Pp. 53-65.
8. Chernyshova Y. S. et al. Optical font recognition in smartphone-captured images and its applicability for ID forgery detection // Eleventh International Conference on Machine Vision (ICMV 2018). – International Society for Optics and Photonics, 2019. Т. 11041. P. 110411J.
9. Polevoy D.V., Aliev M.A., Nikolaev D.P. Choosing the best image of the document owner’s photograph in thevideo stream on the mobile device / ICMV 2020, SPIE 1160540-40-5/ V. 11605, 11605 0F. Pp. 1-9. 2021. DOI: 10.1117/12.2586939.
10. Aliev M. A. et.al. Algorithm for choosing the best frame in a video stream in the task of identity document recognition / Computer Optics. V. 45. № 1. Pp. 101-109. 2021. DOI: 10.18287/2412-6179-CO-811.
11. Ilyukhin S.A., Chernov T.S., Polevoy D.V. Povyshenie tochnosti nejrosetevyh metodov verifikacii lic za schet prostranstvenno-vzveshennoj normalizacii jarkosti izobrazhenija [Improving the Accuracy of Neural Network Methods of Verification of Persons by Spatial-Weighted Normalization of Brightness Image] // ITiVS. 2019. № 4. Pp. 12-20. DOI: 10.14357/20718632190402.
12. Hartl A., Arth C., Schmalstieg D. AR-based hologram detection on security documents using a mobile phone // International Symposium on Visual Computing. Springer, Cham, 2014. С. 335-346.
13. Arlazarov V.V. and others. Sposob detektirovaniya golograficheskih elementov v videopotoke. [Method of detecting holographic elements in a video stream] // Patent RF № 2644513. 2018.
14. Ebner M. Color constancy. John Wiley & Sons, 2007.
15. Vinogradova YU. V., Nikolaev D. P., Slugin D. G. Rassloenie izobrazhenij pechatnyh dokumentov s ispol'zovaniem cvetovoj klasterizacii. [Layering of printed documents images using color clustering] // Informacionnye tekhnologii i vychislitel'nye sistemy. [Information technologies and computing systems] 2015. №. 2. Pp. 40-49.
16. Nikolaev D. P., Nikolayev P. P. Linear color segmentation and its implementation // Computer Vision and Image Understanding. 2004. V. 94. №. 1-3. Pp. 115-139.
17. Buchsbaum G. A spatial processor model for object color perception // Journal of the Franklin institute. 1980. V. 310. №. 1. Pp. 1-26.
18. Kaiming He, Jian Sun and Xiaoou Tang. Single Image Haze Removal Using Dark Channel Prior // IEEE Transactions on Pattern Analysis and Machine Intelligence 33.12. 2011. Pp. 2341—2353. doi: 10.1109/TPAMI.2010.168.
19. Akkaynak D., Treibitz T. Sea-thru: A method for removing water from underwater images // Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019. Pp. 1682-1691.
20. Shepelev D. A., O tochnosti cvetoperedachi pri pokanal'nom modelirovanii podvodnyh izobrazhenij. [The accuracy of color rendering of channel-by-channel underwater images modeling.] // Informacionnye process [Information Processes] 20.3. 2020. Pp. 254—268.
21. Paulus D., Csink L., Niemann H. Color cluster rotation // Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No. 98CB36269). IEEE, 1998. V. 1. Pp. 161-165.
22. Ershov E. I., Asvatov E. N., Nikolaev D. P. Robastnaya ortogonal'naya linejnaya regressiya dlya malomernyh gistogramm. [Robust orthogonal linear regression for lowdimensional histograms] // Sensornye sistemy [Sensory systems]. 2017. V. 31. №. 4. Pp. 331-342
23. Ershov E. I., Terekhin A. P., Nikolaev D. P. Generalization of the fast hough transform for three-dimensional images // Journal of Communications Technology and Electronics. 2018. V. 63. №. 6. Pp. 626-636.
24. Bulatov K.B., Chukalina M.V., Nikolaev D.P. Fast x-ray sum calculation algorithm for computed tomography // Bulletin of the South Ural State University, Series: Mathematical Modelling, Programming and Computer Software, V. 13. № 1. Pp. 95-106. 2020. DOI: 10.14529/mmp200107.
25. Nikolaev D. P. et al. Hough transform: underestimated tool in the computer vision field // Proceedings of the 22th European Conference on Modelling and Simulation. 2008. V. 238. Pp. 246.
26. Gelfand I. M., Gindikin S. G., Graev M. I. Izbrannye zadachi integral'noj geometrii. [Selected Problems of Integral Geometry] // Dobrosvet, 2000.
27. Deriche R. Fast algorithms for low-level vision // IEEE transactions on pattern analysis and machine intelligence. 1990. V. 12. №. 1. Pp. 78-87.
 

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