FACE RECOGNITION TECHNIQUES

Omawiany jest problem rozpoznawania twarzy. Rozważane są główne metody rozpoznawania. Użyta zostaje skalibrowana para stereo dla twarzy oraz obliczanie mapy głębokości poprzez algorytm korelacji. W wyniku takiego, uzyskiwana jest maska twarzy w wymiarze 3D. Użycie trzech antropomorficznych punktów, a następnie skonstruowany systemu współrzędnych zapewnia możliwość nakładania się przetestowanej maski.

Słowa kluczowe:

metody rozpoznawania pary stereo, mapa głębi, algorytm korelacji, funkcje perturbacji, działanie odejmowania

Bibliografia

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

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

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].

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

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

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

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

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

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

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

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

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.

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

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

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.

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

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

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

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.

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

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.

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

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

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

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

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

Romanyuk, O. N., Vyatkin, S. I., Pavlov, S. V., Mykhaylov, P. I., Chekhmestruk, R. Y., & Perun, I. V. (2020). TECHNIKI ROZPOZNAWANIA TWARZY. Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, 10(1), 52-57. https://doi.org/10.35784/iapgos.922

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Numer Tom 10 Nr 1 (2020)

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