G. A. Kukharev, Y. N. Matveev, N. L. Shchegoleva

Read the full article 
Article in Russian


The paper provides analysis of existing approaches to the generating of barcodes and description of the system structure for generating of barcodes from facial images. The method for generating of standard type linear barcodes from facial images is proposed. This method is based on the difference of intensity gradients, which represent images in the form of initial features. Further averaging of these features into a limited number of intervals is performed; the quantization of results into decimal digits from 0 to 9 and table conversion into the standard barcode is done. Testing was conducted on the Face94 database and database of composite faces of different ages. It showed that the proposed method ensures the stability of generated barcodes according to changes of scale, pose and mirroring of facial images, as well as changes of facial expressions and shadows on faces from local lighting. The proposed solutions are computationally low-cost and do not require the use of any specialized image processing software for generating of facial barcodes in real-time systems.

Keywords: facial images, brightness gradients, barcode, real-time systems

1.          Heeter T.W. Method for verifying human identity during electronic sale transactions. Patent US 5878155. Filing date: 05.09.96. Publication date: 02.03.99.
2.          Barcode Tattoos of Scott Blake. Available at: http://www.barcodeart.com/store/wearable/tattoos/ (accessed 24.02.2014).
3.          Dakin S.C., Watt R.J. Biological “bar codes” in human faces. Journal of Vision, 2009, vol. 9, no.4,pp. 1–10.
4.          Goffaux V., Dakin S.C. Horizontal information drives the behavioral signatures of face processing. Frontiers in Psychology, 2010, vol. 1, art. no. 143. doi: 10.3389/fpsyg.2010.00143
5.          Facial Barcodes Help Us Identify People. Available at: http://www.barcodesinc.com/news/?p=92 (accessed 24.02.2014).
6.          Kukharev G.A., Kamenskaya E.I., Matveev Y.N., Shchegoleva N.L. Metody obrabotki i raspoznavaniya izobrazhenii lits v zadachakh biometrii [Methods for face image processing and recognition in biometric applications] / Ed. M.V. Khitrov. St. Petersburg, Politekhnika Publ., 2013, 388 p.
7.          Kukharev G.A., Matveev Y.N., Shchegoleva N.L. Ekspress-metod generatsii schtrikh-koda po izobrazheniyam lits [Express method of barcode generation from facial images].Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2014, no. 2 (90), pp. 99–106.
8.          Querini M., Italiano G.F. Facial biometrics for 2D barcodes. Proc. of the Federated Conference on Computer Science and Information Systems, FedCSIS 2012. Wroclaw, Poland, 2012, art. no. 6354334, pp. 755–762.
9.          QueriniM., Italiano G.F. Facial recognition with 2D color barcodes. International Journal of Computer Science and Application, 2013, vol. 10, no. 1, pp. 78–-97.
10.       Forczmanski P., Kukharev G. Comparative analysis of simple facial features extractors. Journal of Real-Time Image Processing, 2007, vol. 1, no. 4, pp. 239–255. doi: 10.1007/s11554-007-0030-4
11.       Matveev Y.N. Tekhnologii biometricheskoi identifikatsii lichnosti po golosu i drugim modal'nostyam [Technologies of biometric identification of a person by voice and other modalities]. Engineering Journal: Science and Innovations, 2012, no. 3 (3), p. 5.
12.       Collection of Facial Images: Faces94. Available at: http://cswww.essex.ac.uk/mv/allfaces/faces94.html(accessed 24.02.2014).
13.       Algoritm formirovaniya shtrikh-koda EAN-8[Algirithm for EAN-8 barcode generating]. Available at: http://www.cherry-notes.spb.ru/barcode_ean8.htm (accessed 24.02.2014).
14.       BurtD.M., Perrett D.I. Perception of age in adult Caucasian male faces: Computer graphic manipulation of shape and colour information. Proceedings of the Royal Society B: Biological Sciences, 1995, vol. 259, no. 1355, pp. 137–143. doi: 10.1098/rspb.1995.0021
15.       Dosselmann R., Yang X.D. A comprehensive assessment of the structural similarity index. Signal, Image and Video Processing, 2011, vol. 5, no. 1, pp. 81–91. doi: 10.1007/s11760-009-0144-1
Copyright 2001-2017 ©
Scientific and Technical Journal
of Information Technologies, Mechanics and Optics.
All rights reserved.