Hybrid framework for detection of human face based on haar-like feature

  • Authors

    • G Ramkumar Sathyabama Institute of Science and Technology
    • E Logashanmugam Sathyabama Institute of Science and Technology
    2018-08-22
    https://doi.org/10.14419/ijet.v7i3.16227
  • Biometrics, Face Detection, Gabor Filter, Haar Like Features.

  • Abstract

    Augmentation in computer technology has made attainable to prompt incipient video processing practices in territory of biometric identification. Applications embroil face detection and face recognition consolidated to examination framework, signal investigation etc. Face detection is broadly utilized as a part of intuitive user interfaces and assumes an essential part in the area of computer vision. In order to build a fully automated system that can analyze the information in face image, there is a requirement for powerful and productive face detection algorithms. In this paper a framework is proposed for human face detection in images acquired under various illumination conditions. The features established on Gabor filters extracted from local image are applied to be the input of the Haar like classifier. At last, the experiment indicates elite in both accuracy and speed of the created framework.

     

     

  • References

    1. [1] C. Sofuogglu and A. T. Cemgil, "Change-point detection via switching Kalman filters," 2017 25th Signal Processing and Communications Applications Conference (SIU) Antalya, 2017, pp. 1-4https://doi.org/10.1109/SIU.2017.7960439.

      [2] A. Wu, W. S. Zheng and J. H. Lai, "Robust Depth-Based Person Re-Identification," in IEEE Transactions on Image Processing, vol. 26, no. 6, pp. 2588-2603, June 2017https://doi.org/10.1109/TIP.2017.2675201.

      [3] F. Bowen, J. Hu and E. Y. Du, "A Multistage Approach for Image Registration," in IEEE Transactions on Cybernetics, vol. 46, no. 9, pp. 2119-2131, Sept. 2016https://doi.org/10.1109/TCYB.2015.2465394.

      [4] G. Deore, R. Bodhula, V. Udpikar and V. More, "Study of masked face detection approach in video analytics," 2016 Conference on Advances in Signal Processing (CASP), Pune, 2016, pp. 196-200https://doi.org/10.1109/CASP.2016.7746164.

      [5] Paul Viola, Michael J Jones. Robust real-time face detection[J].IJCV, 2004, 57(2):137-154

      [6] Harsh Nanda, LarryDavis. Probabilistic Template Based PedestrianDetection in Infrared Videos [D]. University of Maryland, College Park,MD-20742

      [7] C. Ding, C. Xu and D. Tao, "Multi-Task Pose-Invariant Face Recognition," in IEEE Transactions on Image Processing, vol. 24, no. 3, pp. 980-993, March 2015https://doi.org/10.1109/TIP.2015.2390959.

      [8] S. Liao, A. K. Jain and S. Z. Li, "A Fast and Accurate Unconstrained Face Detector," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 2, pp. 211-223, Feb. 1 2016https://doi.org/10.1109/TPAMI.2015.2448075.

      [9] ManminderSinghA.S.AroraDr. "Varying Illumination and Pose Conditions in Face Recognition" in Procedia Computer ScienceVolume 85, 2016, Pages 691-695 ELSEVIER

      [10] Ke Yan, Youbin Chen and D. Zhang, "Gabor Surface Feature for face recognition," The First Asian Conference on Pattern Recognition, Beijing, 2011, pp. 288-292.https://doi.org/10.1109/ACPR.2011.6166553.

      [11] M. Da'san, A. Alqudah and O. Debeir, "Face detection using Viola and Jones method and neural networks," 2015 International Conference on Information and Communication Technology Research (ICTRC), Abu Dhabi, 2015, pp. 40-43https://doi.org/10.1109/ICTRC.2015.7156416.

      [12] S. Caharel, M. Ramon and B. Rossion, "Face Familiarity Decisions Take 200 msec in the Human Brain: Electrophysiological Evidence from a Go/No-go Speeded Task," in Journal of Cognitive Neuroscience, vol. 26, no. 1, pp. 81-95, Jan. 2014https://doi.org/10.1162/jocn_a_00451.

      [13] Yi-Chong Zeng, "Automatic extraction of useful scenario information for dramatic videos," 2013 9th International Conference on Information, Communications & Signal Processing, Tainan, 2013, pp. 1-5

      [14] G. Ramkumar and E. Logashanmugam, "An effectual face tracking based on transformed algorithm using composite mask," 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Chennai, 2016, pp. 1-5.https://doi.org/10.1109/ICCIC.2016.7919614.

      [15] K. Zhang, Q. Liu, Y. Wu and M. H. Yang, "Robust Visual Tracking via Convolutional Networks Without Training," in IEEE Transactions on Image Processing, vol. 25, no. 4, pp. 1779-1792, April 2016https://doi.org/10.1109/TIP.2016.2531283.

      [16] J. Geng, J. Fan, H. Wang, X. Ma, B. Li and F. Chen, "High-Resolution SAR Image Classification via Deep Convolutional Autoencoders," in IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 11, pp. 2351-2355, Nov. 2015https://doi.org/10.1109/LGRS.2015.2478256.

      [17] P. Liu and D. Liu, "Filter-based compounded delay estimation with application to strain imaging," in IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 58, no. 10, pp. 2078-2095, October 2011https://doi.org/10.1109/TUFFC.2011.2058.

      [18] P. Wang, C. Shen, N. Barnes and H. Zheng, "Fast and Robust Object Detection Using Asymmetric Totally Corrective Boosting," in IEEE Transactions on Neural Networks and Learning Systems, vol. 23, no. 1, pp. 33-46, Jan. 2012https://doi.org/10.1109/TNNLS.2011.2178324.

      [19] Jean Paul, Niyoyita Zhao Hui Tang,Jin Ping Liu “Multi-view Face Detection Using Six Segmented Rectangular Features†The Sixth International Symposium on Neural Networks,Springer, Berlin, Heidelberg 2009

      [20] S. Liao, A. K. Jain and S. Z. Li, "A Fast and Accurate Unconstrained Face Detector," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 2, pp. 211-223, Feb. 1 2016https://doi.org/10.1109/TPAMI.2015.2448075.

      [21] W. ZHANG, X. ZHAO, J. M. Morvan and L. Chen, "Improving Shadow Suppression for Illumination Robust Face Recognition," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PP, no. 99, pp. 1-1.

      [22] V. Jain and E. Learned-Miller, “FDDB: A benchmark for face detection in unconstrained settings,†University of Massachusetts, Amherst, Tech. Rep. UM-CS-2010-009, 2010.

  • Downloads

  • How to Cite

    Ramkumar, G., & Logashanmugam, E. (2018). Hybrid framework for detection of human face based on haar-like feature. International Journal of Engineering & Technology, 7(3), 1786-1790. https://doi.org/10.14419/ijet.v7i3.16227

    Received date: 2018-07-25

    Accepted date: 2018-07-25

    Published date: 2018-08-22