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Automatic Facial Expression Exaggeration System with Parallelized Implementation on a Multi-Core Embedded Computing Platform

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Abstract

In this paper, we propose an automatic facial expression exaggeration system, which consists of face detection, facial expression recognition, and facial expression exaggeration components, for generating exaggerated views of different expressions for an input face video. In addition, the parallelized algorithms for the automatic facial expression exaggeration system are developed to reduce the execution time on a multi-core embedded system. The experimental results show satisfactory expression exaggeration results and computational efficiency of the automatic facial expression exaggeration system under cluttered environments. The quantitative experimental comparisons show that the proposed parallelization strategies provide significant computational speedup compared to the single-processor implementation on a multi-core embedded platform.

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References

  1. 1.

    Dubey, P. (2005). A platform 2015 workload model recognition, mining and synthesis moves computers to the Era of Tera. Intel Corp. White Paper.

  2. 2.

    Lin, T.-J., Lin, C.-N., Tseng, S.-Y., Chu, Y.-H., Wu, A.-Y. (2008). Overview of itri pac project—from vliw dsp processor to multi-core computing platform. IEEE international symposium on VLSI design, automation and test (pp. 188–191).

  3. 3.

    Viola, P., & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1, IV511–IV518.

  4. 4.

    Turk, M., & Pentland, A. (1991). Eigenfaces for recognition. Jounal of Cognitive Neuroscience, 3(1), 71–86.

  5. 5.

    Rowley, H., Baluja, S., Kanade, T. (1998). Neural network-based face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(1), 23–38.

  6. 6.

    Osuna, E., Freund, R., Girosit, F. (1997). Training support vector machines: an application to face detection. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 130–136.

  7. 7.

    Yang, M.-H., Kriegman, D., Ahuja, N. (2002). Detecting faces in images: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(1), 34–58.

  8. 8.

    Sharma, B., Thota, R., Vydyanathan, N., Kale A. (2009). Towards a robust, real-time face processing system using cuda-enabled GPUs. In International conference on high performance computing.

  9. 9.

    Chen, S.-K., Lin, T.-J., Liu, C.-W. (2009). Parallel object detection on multicore platforms. In IEEE workshop on signal processing systems.

  10. 10.

    Sun, Y., & An, Y. (2010). Research on the embedded system of facial expression recognition based on HMM. In IEEE international conference on information management and engineering(ICIME).

  11. 11.

    Ekman, P., & Friesen, W.V. (1971). Constants across cultures in the face and emotion. Journal of Personality and Social Psychology, 17(2), 124–129.

  12. 12.

    Song, M., Tao, D., Liu, Z., Li, X., Zhou, M. (2010). Image ratio features for facial expression recognition application. IEEE Transactions on Systems Man and Cybernetics Part B: Cybernetics, 40(3), 779–788.

  13. 13.

    Zhao, G., & Pietikainen, M. (2007). Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(6), 915–928.

  14. 14.

    Lee, Y.-B., Moon, S.-B., Kim, Y.-G. (2005). Face and facial expression recognition with an embedded system for human-robot interaction. Affective Computing and Intelligent Interaction, 3784, 271–278.

  15. 15.

    Liang, L., Chen, H., Xu, Y.-Q., Shum, H.-Y. (2002). Example-based caricature generation with exaggeration. Society Proceedings of the Pacific Conference on Computer Graphics and Applications (pp. 386–393).

  16. 16.

    Mo, Z., Lewis, P., Neumann, U. (2004). Improved automatic caricature by feature normalization and exaggeration. ACM SIGGRAPH sketches (p. 57).

  17. 17.

    Chen, H., Xu, Y.-Q., Shum, H.-y., Zhu, S.-C., Zheng, N.-N. (2001). Example-based facial sketch generation with non-parametric sampling. IEEE International Conference on Computer Vision, 2, 433–438.

  18. 18.

    Liu, J., Chen, Y., Gao, W. (2006). Mapping learning in eigenspace for harmonious caricature generation. Proceedings of ACM international conference on Multimedia, 683–686.

  19. 19.

    Chang, Y., Hu, C., Turk, M. (2003). Manifold of facial expression. AMFG Workshop.

  20. 20.

    Chang, Y., Hu, C., Turk, M. (2004). Probabilistic expression analysis on manifolds. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2, II-520–II-527.

  21. 21.

    Hu, C., Chang, Y., Feris, R., Turk, M. (2004). Manifold based analysis of facial expression. IEEE workshop on face processing in video (p. 81).

  22. 22.

    Periaswamy, S., & Farid, H. (2003). Elastic registration in the presence of intensity variations. IEEE Transactions on Medical Imaging, 22(7), 865–874.

  23. 23.

    Chen, D.C.-W., Liao, I-T., Lee, J.-K., Chen, W.-F., Tseng, S.-Y., Jen, C.-W. (2006). PAC DSP core and application processors. IEEE international conference on multimedia and expo (pp. 289–292).

  24. 24.

    Lin, Y.-C., Tang, C.-L., Wu, C.-J., Hung, M.-Y., You, Y.-P., Moo, Y.-C., Chen, S.-Y., Lee, J.-K. (2006). Compiler supports and optimizations for pac vliw dsp processors (pp. 466–474). Workshop on Languages and Compilers for Parallel Computing.

  25. 25.

    Sim, T., Baker, S., Bsat, M. (2003). The cmu pose, illumination, and expression database. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(12), 1615–1618.

  26. 26.

    Intel Corporation (2005). Computer intenstive, highly parallel application and uses. Intel Technology Journal, 9(2), 5–6.

  27. 27.

    Liao, C.-T., Chuang, H.-J., Duan, C.-H., Lai, S.-H. (2010). Learning spatial weighting via quadratic programming for facial expression analysis. IEEE International Conference on Computer Vision and Pattern Recognition Workshop (CVPRW) (pp. 86–93).

  28. 28.

    Sirovich, L., & Kirby, M. (1987). Low-dimensional procedure for the characterization of human faces. Journal of the Optical Society of America, A4(3), 519–524.

  29. 29.

    Kisacanin, B. (2005). Examples of low-level computer vision on media processors. IEEE Internation Conference on Computer Vision and Pattern Recognition Workshop (CVPRW) (p. 135).

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Correspondence to Te-Feng Su.

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Su, T., Duan, C., Wang, S. et al. Automatic Facial Expression Exaggeration System with Parallelized Implementation on a Multi-Core Embedded Computing Platform. J Sign Process Syst 75, 155–168 (2014). https://doi.org/10.1007/s11265-013-0751-5

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Keywords

  • Face detection
  • Facial expression recognition
  • Facial expression exaggeration
  • Multi-Core embedded system