Advertisement

FPGA Based Face Detection Using Local Ternary Pattern Under Variant Illumination Condition

  • Jin Young Byun
  • Jae Wook Jeon
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 474)

Abstract

This paper presents the design and implementation of real-time face detection using Local Ternary Pattern (LTP). First, an input image is transferred by the Camlink interface and the image is then downscaled for face detection. A tree-structured cascade of classifiers is used for face detection. We implemented the proposed hardware architecture on a Xilinx Virtex-7 FPGA and the processing speed was adjusted to the frame rate of the camera. The size of the input images is 640 × 480 (VGA) and a larger size can be used without performance loss.

Keywords

Face detection Local ternary pattern (LTP) 

Notes

Acknowledgments

This work was supported by the Samsung Research Funding Center of Samsung Electronics under Project Number SRFC-IT1402-12.

References

  1. 1.
    Yang, B., et al.: Aggregate channel features for multi-view face detection. In: 2014 IEEE International Joint Conference on Biometrics (IJCB). IEEE (2014)Google Scholar
  2. 2.
    Elmer, P., et al.: Exploring compression impact on face detection using haar-like features. In: Scandinavian Conference on Image Analysis. Springer, Cham (2015)Google Scholar
  3. 3.
    Liao, S., Jain, A.K., Li, S.Z.: A fast and accurate unconstrained face detector. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 211–223 (2016)CrossRefGoogle Scholar
  4. 4.
    Chen, D., et al.: Joint cascade face detection and alignment. In: European Conference on Computer Vision. Springer, Cham (2014)Google Scholar
  5. 5.
    Farfade, S.S, Saberian, M.J., Li, L.-J.: Multi-view face detection using deep convolutional neural networks. In: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval. ACM, Haider (2015)Google Scholar
  6. 6.
    Waqas, H., et al.: A survey on face detection and recognition approaches. Res. J. Recent Sci. 3(4), 56–62 (2014). ISSN: 2277-2502Google Scholar
  7. 7.
    Cho, J., Mirzaei, S., Oberg, J., Kastner, R.: FPGA-based face detection system using Haar classifiers. In: Proceedings of 17th ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, pp. 103–112 (2009)Google Scholar
  8. 8.
    Yu, W., Bing, X., Chareonsak, C.: FPGA implementation of AdaBoost algorithm for detection of face biometrics. In: IEEE International Workshop on Biomedical Circuits and Systems. IEEE (2004)Google Scholar
  9. 9.
    Gao, C., Lu, S.-L.: Novel FPGA based Haar classifier face detection algorithm acceleration. In: International Conference on Field Programmable Logic and Applications, FPL. IEEE (2008)Google Scholar
  10. 10.
    Mahale, G., et al.: Hardware solution for real-time face recognition. In: 28th International Conference on VLSI Design (VLSID). IEEE (2015)Google Scholar
  11. 11.
    Jin, S., et al.: An FPGA-based parallel hardware architecture for real-time face detection using a face certainty map. In: 20th IEEE International Conference on Application-specific Systems, Architectures and Processors, ASAP. IEEE (2009)Google Scholar
  12. 12.
    Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. In: International Workshop on Analysis and Modeling of Faces and Gestures, pp. 168–182. Springer, Heidelberg (2007)Google Scholar
  13. 13.
    Ren, J., Jiang, X., Yuan, J.: Relaxed local ternary pattern for face recognition. In: ICIP (2013)Google Scholar
  14. 14.
    Jun, B., Kim, D.: Robust real-time face detection using face certainty map. Lecture Notes Computer Science. vol. 4642, pp. 29–38. Springer, Heidelberg (2007)Google Scholar
  15. 15.
    Benjamin, W., et al.: Component-based face recognition with 3D morphable models. In: Conference on Computer Vision and Pattern Recognition Workshop, CVPRW 2004. IEEE (2004)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.School of Information and Communication EngineeringSungkyunkwan UniversitySuwonKorea

Personalised recommendations