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.uaVinnitsa 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 subtractionReferences
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
Authors
Olexandr N. RomanyukVinnitsa National Technical University Ukraine
http://orcid.org/0000-0002-2245-3364
Authors
Sergey I. VyatkinInstitute of Automation and Electrometry SB RAS, Novosibirsk Russian Federation
http://orcid.org/0000-0002-1591-3588
Authors
Sergii V. Pavlovpsv@vntu.edu.ua
Vinnitsa National Technical University Ukraine
http://orcid.org/0000-0002-0051-5560
Authors
Roman Y. Chekhmestruk3D GENERATION UA, Vinnitsa Ukraine
http://orcid.org/0000-0002-5362-8796
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