Synthetic data generation models and algorithms for training image recognition algorithms using the Viola-Jones framework
A.V. Akimov, A.A. Sirota

 

Voronezh State University, Voronezh, Russia

Full text of article: Russian language.

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Abstract:
The paper describes mathematical models and algorithms of warping grid functions with discrete parameters. For images, three warping models, applied to the generation of extra training data to build face recognition algorithms, are examined: the one based on harmonic functions, the one based on offsetting user-specified control point coordinates, and the one based on the computation of the optical flow between entropy-filtered images. For the training sets, both initial and those synthetically generated using the above three models, learning of face detection algorithms based on the Viola-Jones framework was performed and corresponding detection rates were compared. It is shown that this approach is applicable for synthetic data generation when training image recognition algorithms for recognition of objects characterized by inherent structure.

Keywords:
image recognition, warping, interpolation, RBF, optical flow, entropy, Viola-Jones framework.

Citation:
Akimov AV, Sirota AA. Synthetic data generation models and algorithms for training image recognition algorithms using the Viola-Jones framework. Computer Optics 2016; 40(6): 911-918. DOI: 10.18287/2412-6179-2016-40-6-911-918.

References:

  1. Guo H, Viktor HL. Learning from imbalanced data sets with boosting and data generation: the DataBoost IM approach. ACM SIGKDD Explorations Newsletter 2004; 6(1): 30-39. DOI: 10.1145/1007730.1007736.
  2. Chawla N, Bowyer K, Hall L, Kegelmeyer W. SMOTE: synthetic minority over-sampling technique. Journal of Artificial Intelligence Research 2002; 16(1): 321-357. DOI: 10.1613/jair.953.
  3. Zhukovsky A, Usilin S, Tarasova N, Nikolaev D. Synthetic training sets based on real data in the problems of image recognition [In Russian]. Proceedings of the Conference “Information Technology and Systems (ITaS’12)”, Moscow 2012; 377-382.
  4. Viola P, Jones M. Rapid object detection using a boosted cascade of simple features. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2001; 1: 511-518. DOI:  10.1109/CVPR.2001.990517.
  5. Viola P, Jones M. Robust real-time face detection. International Journal of Computer Vision 2004; 57(2): 137-154. DOI: 10.1023/B:VISI.0000013087.49260.fb.
  6. Kalinovskii IA, Spitsyn VG. Review and testing of frontal face detectors. Computer Optics 2016; 40(1): 99-111. DOI: 10.18287/2412-6179-2016-40-1-99-111.
  7. Freund Y, Schapire R. A Short Introduction to Boosting. Journal of Japanese Society for Artificial Intelligence 1999; 14(5): 771-780.
  8. Akimov AV, Sirota AA. Design and analysis of algorithms for image recognition based on the method of Viola-Jones using computing technology on GPU CUDA [In Russian]. Vestnik VSU, Series: System Analysis and Information Technology 2014; 3: 100-108.
  9. Akimov AV, Dryuchenko MA, Sirota AA. Models and algorithms for making distorting distortion in images using radial basis functions [In Russian]. Vestnik VSU, Series: System Analysis and Information Technology 2014; 1: 130-137.
  10. Wolberg G. Image Morphing Survey. The Visual Computer 1998; 14(8): 360-372. DOI: 10.1007/s003710050148.
  11. Steyvers M. Morphing Techniques for Manipulating Face Images. Behavior Research Methods, Instruments, & Com­puters 1999; 31(2): 359-369. DOI: 10.3758/BF03207733.
  12. Arad N, Dyn N, Reisfeld D, Yeshurun Y. Image warping by radial basis functions: applications to facial expressions. CVGIP: Graph Models Image Processing 1994; 56(2): 161-172. DOI: 10.1006/cgip.1994.1015.
  13. Sizikov VS. Robust methods of processing measurement results. Tutorial [In Russian]. Saint-Petersburg: “SpecLit” Pulisher; 1999.
  14. Brown LG. A survey of image registration techniques. ACM Computing Surveys 1992; 24(4): 325-376. DOI: 10.1145/146370.146374.
  15. Barron J, Fleet D, Beauchemin S. Performance of Optical Flow Techniques. International Journal of Computer Vision 1994; 12(1): 43-77. DOI: 10.1007/BF01420984.
  16. Horn B, Schunk B. Determining Optical Flow. Artificial Intelligence 1981; 17(1-3): 185-203. DOI: 10.1016/0004-3702(81)90024-2.
  17. Farneback, G. Two-Frame Motion Estimation Based on Polynomial Expansion. Proc SCIA'03 2003; 363-370. DOI: 10.1007/3-540-45103-X_50.
  18. Gonzalez RC, Woods RE, Eddins SL. Digital image processing using MATLAB. 2nd ed. New Jersey: Prentice Hall; 2009. ISBN: 978-0982085400.
  19. CMU/VASC image database: Frontal face images. Source: <http://vasc.ri.cmu.edu/idb/html/face/frontal_images/in­dex.html>.

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