FACE RECOGNITION TECHNIQUES

Olexandr N. Romanyuk


Vinnitsa National Technical University (Ukraine)
http://orcid.org/0000-0002-2245-3364

Sergey I. Vyatkin


Institute of Automation and Electrometry SB RAS, Novosibirsk (Russian Federation)
http://orcid.org/0000-0002-1591-3588

Sergii V. Pavlov

psv@vntu.edu.ua
Vinnitsa National Technical University (Ukraine)
http://orcid.org/0000-0002-0051-5560

Pavlo I. Mykhaylov


3D GNERATION GmbH, Dortmund (Germany)
http://orcid.org/0000-0001-5861-5970

Roman Y. Chekhmestruk


3D GENERATION UA, Vinnitsa (Ukraine)
http://orcid.org/0000-0002-5362-8796

Ivan V. Perun


3D GENERATION UA, Vinnitsa (Ukraine)
http://orcid.org/0000-0001-7402-4417

Abstract

The problem of face recognition is discussed. The main methods of recognition are considered. The calibrated stereo pair for the face and calculating the depth map by the correlation algorithm are used. As a result, a 3D mask of the face is obtained. Using three anthropomorphic points, then constructed a coordinate system that ensures a possibility of superposition of the tested mask.


Keywords:

methods of recognition stereo pair, depth map, correlation algorithm, perturbation functions, operation of subtraction

Amann M. C., Bosch T. M., Lescure M., Myllylae R. A., Rioux M.:Laser ranging: a critical review of unusual techniques for distance measurement. Optical Engineering 40(1)/2001, 10–19, [http://doi.org/10.1117/1.1330700].
DOI: https://doi.org/10.1117/1.1330700   Google Scholar

Belhumeur P. N., Hespanha J. P., Kriegman D. J.: Eigen faces vs. Fisher faces: Recognition using Class Specific Linear Projection. IEEE Transactions on pattern analysis and machine intelligence 19(7)/1997, 711–720, [http://doi.org/10.1109/34.598228].
DOI: https://doi.org/10.1109/34.598228   Google Scholar

Butime J., Gutierrez I., GaloCorzo L., Flores C.: Espronceda. 3D reconstruction methods, a survey. Proceedings of the First International Conference on Computer Vision Theory and Applications, 2006, 457–463, [http://doi.org/0.5220/0001369704570463].
  Google Scholar

Chien C. H., Aggarwal J. K.: Identification of 3D Objects from Multiple Silhouettes Using Quadtrees / Octrees. Computer Vision Graphics And Image Processing 36(2–3)/1986, 256–273.
DOI: https://doi.org/10.1016/0734-189X(86)90078-2   Google Scholar

Edwards G. J., Cootes T. F., Taylor C. J.: Face recognition using active appearance models. European Conference on Computer Vision, 1998, 581-595. [http://doi.org/10.1007/BFb0054766].
DOI: https://doi.org/10.1007/BFb0054766   Google Scholar

Jecić S., Drvar N.: 3D Shape Measurement Influencing Factors. NDT – Competence & Safety, Zagreb 2004, 109–116.
  Google Scholar

Jolliffe I. T.: Principal component analysis, second edition. Springer, New York 2002.
  Google Scholar

Lades M., Vorbruggen J.C., Buhmann J., et al.: Distortion Invariant Object Recognition in the Dynamic Link Architecture. IEEE Transactions on Computers 42(3)/1993 300–311, [http://doi.org/10.1109/12.210173].
DOI: https://doi.org/10.1109/12.210173   Google Scholar

Lawrence S., Giles C.L., et al.: Back. Face Recognition: A Convolutional Neural-Network Approach. IEEE Transactions on Neural Networks 8(1)/1997, 98–113, [http://doi.org/10.1109/72.554195].
DOI: https://doi.org/10.1109/72.554195   Google Scholar

Lipton L.: Foundations of the Stereoscopic Cinema – A Study in Depth. Van Nostrand Reinhold, New York 1982.
  Google Scholar

Martin W. N., Aggarwal J. K.: Volumetric Descriptions of Objects from Multiple Views. IEEE Transactions on Pattern Analysis and Machine Intelligence 5(2)/1983, 150–158.
DOI: https://doi.org/10.1109/TPAMI.1983.4767367   Google Scholar

Nefian A. V.: A hidden Markov model-based approach for face detection and recognition. A Proposal for a Doctoral Dissertation. Georgia Institute of Technology 1998.
  Google Scholar

Niem W.: Robust and Fast Modeling of 3D Natural Objects from Multiple Views. Proceedings Image and Video Processing II 2182/1994, 388–397.
DOI: https://doi.org/10.1117/12.171088   Google Scholar

Prabhu U., Seshadri K.: Facial Recognition Using Active Shape Models, Local Patches and Support Vector Machines, 2009.
  Google Scholar

Reutebuch S. E., Andersen H., Mcgaughey R. J., Forest L.: Light Detection and Ranging (LIDAR): An Emerging Tool for Multiple Resource Inventory. J. For. 103(6)/2005, 286–292.
  Google Scholar

Siudak M., Rokita P.: A survey of passive 3D reconstruction methods on the basis of more than one image. Machine Graphics & Vision 23(3/4)/2014, 57–11.
DOI: https://doi.org/10.22630/MGV.2014.23.3.5   Google Scholar

Szeliski R.: Shape from rotation. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'91), 1991, 625–630.
  Google Scholar

Taigman Y., Yang M., et al.: Deep Face: Closing the gap to human level performance in face verification. IEEE Conference on Computer Vision and Pattern Recognition, 2014, 1701–1708.
DOI: https://doi.org/10.1109/CVPR.2014.220   Google Scholar

Vedula S., Rander P., Saito H., Kanade T.: Modeling, Combining, and Rendering Dynamic Real-World Events From Image Sequences. Proc. 4th Conference on Virtual Systems and Multimedia (VSMM98), 1998, 326–332.
  Google Scholar

Vyatkin S. I., Romanyuk A. N., Gotra Z. Y., et al.: Offsetting, relations and blending with perturbation functions. Proc. of SPIE 10445/2017, 104452B.
DOI: https://doi.org/10.1117/12.2280983   Google Scholar

Vyatkin S. I., Romanyuk S. A., Pavlov S. V., Necheporyk M. L.: Face Identification Algorithms and its using. Modern Engineering and Innovative Technologies 5/2018, 111–115.
  Google Scholar

Vyatkin S. I.: Complex surface modeling using perturbation functions. Optoelectronics, instrumentation and data processing 43/2007, 226–231.
DOI: https://doi.org/10.3103/S875669900703003X   Google Scholar

Vyatkin S. I.: Method of face recognition using of scalar perturbation functions and set-theoretic operation of subtraction. Optoelectronics, instrumentation and data processing 52(1)/2016, 1–7.
DOI: https://doi.org/10.3103/S8756699016010076   Google Scholar

Wiskott L., Fellous J. M., Kruger N., et al.: Face Recognition by Elastic Bunch Graph Matching. Proc. of International Conference on Image Processing 1/1997, 129–132, [http://doi.org/10.1109/ICIP.1997.647401].
DOI: https://doi.org/10.1109/ICIP.1997.647401   Google Scholar

Wójcik W., Pavlov S., Kalimoldayev M.: Information Technology in Medical Diagnostics II. Taylor & Francis Group, London 2019.
DOI: https://doi.org/10.1201/9780429057618   Google Scholar

Rawal R., Yadav V., Sharma S.: Radar – a brief study. International Journal of Innovative Research and Technology 1(12)/2015, 1017–1020.
  Google Scholar

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Published
2020-03-30

Cited by

Romanyuk, O. N., Vyatkin, S. I., Pavlov, S. V., Mykhaylov, P. I., Chekhmestruk, R. Y., & Perun, I. V. (2020). FACE RECOGNITION TECHNIQUES. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 10(1), 52–57. https://doi.org/10.35784/iapgos.922

Authors

Olexandr N. Romanyuk 

Vinnitsa National Technical University Ukraine
http://orcid.org/0000-0002-2245-3364

Authors

Sergey I. Vyatkin 

Institute of Automation and Electrometry SB RAS, Novosibirsk Russian Federation
http://orcid.org/0000-0002-1591-3588

Authors

Sergii V. Pavlov 
psv@vntu.edu.ua
Vinnitsa National Technical University Ukraine
http://orcid.org/0000-0002-0051-5560

Authors

Pavlo I. Mykhaylov 

3D GNERATION GmbH, Dortmund Germany
http://orcid.org/0000-0001-5861-5970

Authors

Roman Y. Chekhmestruk 

3D GENERATION UA, Vinnitsa Ukraine
http://orcid.org/0000-0002-5362-8796

Authors

Ivan V. Perun 

3D GENERATION UA, Vinnitsa Ukraine
http://orcid.org/0000-0001-7402-4417

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