[9] Devjatkov V.V., Lychkov I.I. Simulation and the analysis of situations in the virtual
environment of moving objects.
Vestn. Mosk. Gos. Tekh. Univ. im. N. E. Baumana,
Priborostr.
[Herald of the Bauman Moscow State Tech. Univ., Instrum. Eng.], 2013,
no. 3, pp. 26–42 (in Russ.).
[10] Sikka K., Dykstra K., Sathyanarayana S., Littlewort G., Bartlett M. Multiple kernel
learning for emotion recognition in the wild.
Proc. 15th ACM on Int. Conf. on
Multimodal Interaction (ICMI ’13)
, ACM, New York, USA, pp. 517–524.
[11] Koelstra S., Pantic M., Patras I. A Dynamic Texture Based Approach to Recognition
of Facial Actions and Their Temporal Models.
IEEE Trans. on Pattern Analysis and
Machine Intelligence
, 2010, pp. 1940–1954.
[12] Valstar M.F., Pantic M. Induced Disgust, Happiness and Surprise: an Addition to the
MMI Facial Expression Database.
Proc. Int. Language Resources and Evaluation
Conf.
, Malta, 2010, pp. 65–70.
[13] Lucey P., Cohn J., Kanade T., Saragih J., Ambadar Z., Matthews I. The Extended
Cohn-Kanade Dataset (CK+): a complete dataset for action unit and emotion-specified
expression.
Proc. IEEE Comp. Soc. Conf. on CVPR Workshops
, 2010, pp. 94–101.
[14] Knyazev B. Human nonverbal behavior multi-sourced ontological annotation.
Proc.
Int. Workshop on Video and Image Ground Truth in Comp. Vision Appl. (VIGTA ’13)
,
2013, Article 2, рp. 1–8.
[15] Kashapova L.H., Latysheva E.Ju., Spiridonov I.N. Discriminant Analysis of Two-
Dimensional Gabor Features for Facial Expression Recognition.
Meditsinskaya
Tekhnika
[Biomedical Engineering, 2012, iss. 3, vol. 46, pp. 89–92], 2012, vol. 46,
no. 3, pp. 1–4 (in Russ.).
[16] Wiskott L., Fellous J.-M., Kruger N., von der Malsburg C. Face Recognition by
Elastic Bunch Graph Matching.
IEEE Trans. on Pattern Analysis and Machine
Intelligence
, 1997, vol. 17, no. 7, pp. 775–779.
[17] Basu A., Blanning R.W.
Metagraphs and Their Applications. Integrated Series in
Information Systems
. Springer, 2007, vol. 15, no. VIII, 172 р.
[18] Skurihin A.V. Recursive and hierarchical representation of one-dimensional fractal-
like signals.
Tr. S.-Peterburgskogo Ins. Inf. i Avtomat., RAN (SPIIRAN)
[Proc. SPb
Inst. Inf. and Autom., Russ. Ac. Sc. (SPIIRAS)], 2003, no. 1 (3), pp. 107–117 (in
Russ.).
[19] Bolotova Ju.A., Spicyn V.G., Fomin A.Je. Model application of hierarchical
temporary memory in recognitions of images.
Izv. Tomskogo Politehnicheskogo Un.
[Bulletin of the Tomsk Polytechnic Un.], 2011, vol. 318, no. 5, pp. 60–63 (in Russ.).
[20] Intriligator M.D. Mathematical Optimization and Economic Theory. Series: Classics
in Applied Mathematics (Book 39). Prentice-Hall, 1971. 571 p. (Russ. Ed.: Intriligator
M. Matematicheskie metody optimizacii i jekonomicheskaja teorija. Progress Publ.,
1975. 607 p.).
[21] Bishop Chr. M. Pattern Recognition and Machine Learning (Information Science and
Statistics). Springer-Verlag, New York, Inc., Secaucus, NJ, USA, 2006.
Статья поступила в редакцию 24.02.2014
Борис Александрович Князев — аспирант кафедры “Системы обработки информации
и управление” МГТУ им. Н.Э. Баумана, инженер НИИЦ БТ МГТУ им. Н.Э. Баумана.
Автор девяти научных работ в области искусственного интеллекта, компьютерного
зрения, параллельных вычислений.
МГТУ им. Н.Э. Баумана, Российская Федерация, 105005, Москва, 2-я Бауманская ул.,
д. 5.
ISSN 0236-3933. Вестник МГТУ им. Н.Э. Баумана. Сер. “Приборостроение”. 2014. № 4 53
