Person Recognition by Hilbert Pair of Wavelets using Facial Images

  • Authors

    • Hemalatha c St. Peter's University
    • Logashanmugam E Sathyabama University
    2018-07-04
    https://doi.org/10.14419/ijet.v7i3.11482
  • DTMBWT, Face Recognition, Facial Images, KNN Classifier, Sub-Bands.

  • Abstract

    In human identification, the face acts as an important tool that carries the identity of each person. The human mind has the ability to re-cognize faces after the first view of a human face. Though there are many types of face detection/recognition system found no method can give the 100% accurate outputs. In this proposed system we are implementing and analyzing a new method that can be used for person recognition system that can produce better output accuracies. In the proposed system of person recognition method, one of the robust wavelet transform methods is used for the extraction of the features from the original images. The wavelet type used is known as the Dual Tree M-Band Wavelet Transform (DTMBWT) method. Using this transform the low and high sub-bands is obtained. These low and high sub-band coefficients are given as the input for the classification purpose. The sub-band obtained from the DTMBWT transform is given as the inputs for the classification purpose. The classification process is done using the K-Nearest Neighbor (KNN) classifier scheme. The system is implemented by using the facial images from the ORL database. By using this dataset images the performance measures of the proposed system is calculated in the form of graphical results such as Receiver Operating Characteristic (ROC), Inverse ROC and Expected Performance Curve (EPC) curves. Results show that proposed DTMBWT based face recognition provides better results than other approaches.

     

     

  • References

    1. [1] Uzun-Per M &Gokmen M (2018), Face recognition with Patch-based Local Walsh Transform. Signal Processing: Image Communication 61, 85-96. https://doi.org/10.1016/j.image.2017.11.003.

      [2] Zhu Y, Zhu C & Li X (2018), improved principal component analysis and linear regression classification for face recognition. Signal Processing 145, 175-182.

      https://doi.org/10.1016/j.sigpro.2017.11.018.

      [3] Nanni L, Lumini a &Brahnam S (2017), Ensemble of texture descriptors for face recognition obtained by varying feature transforms and preprocessing approaches. Applied Soft Computing 61, 8-16. https://doi.org/10.1016/j.asoc.2017.07.057.

      [4] Cao F, Feng X & Zhao J (2017), sparse representation for robust face recognition by dictionary decomposition. Journal of Visual Communication and Image Representation 46, 260-268.

      https://doi.org/10.1016/j.jvcir.2017.04.007.

      [5] Liu X, Lu L, Shen Z & Lu K (2018) a novel face recognition algorithm via weighted kernel sparse representation. Future Generation Computer Systems 80, 653-663.

      https://doi.org/10.1016/j.future.2016.07.007.

      [6] Pan J, Wang XS & Cheng YH (2016), Single-Sample Face Recognition Based on LPP Feature Transfer. IEEE Access 4, 2873-2884. https://doi.org/10.1109/ACCESS.2016.2574366.

      [7] Li H, Shen F, Shen C, Yang Y & Gao Y (2016), Face recognition using linear representation ensembles. Pattern Recognition 59, 72-87. https://doi.org/10.1016/j.patcog.2015.12.011.

      [8] Chen Y & Su J (2017), Sparse embedded dictionary learning on face recognition. Pattern Recognition 64, 51-59.

      https://doi.org/10.1016/j.patcog.2016.11.001.

      [9] Nguyen HT &Caplier A (2013), Local Patterns of Gradients (LPOG) for Face Recognition. IEEE Transactions on Information Forensics and Security 1-13.

      [10] Wang J, Lu C, Wang M, Li P, Yan S & Hu X (2014), robust face recognition via adaptive sparse representation. IEEE transactions on cybernetics 44, 12, 2368-2378.

      https://doi.org/10.1109/TCYB.2014.2307067.

      [11] Selesnick IW, Baraniuk RG & Kingsbury NC (2005), The dual-tree complex wavelet transforms. IEEE signal processing magazine 22, 6, 123-151.

      [12] http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html

      [13] Sukhija P, Behal S & Singh P (2016), Face recognition system using genetic algorithm. Procedia Computer Science 85, 410-417.

      [14] Ren D, Hui M, Hu N & Zhan T (2017), A Weighted Sparse Neighbor Representation Based on Gaussian kernel function to Face Recognition. Optik-International Journal for Light and Electron Optics, 1-15.

      [15] Leng B, Yu K & Jingyan QIN (2017), Data augmentation for unbalanced face recognition training sets. Neurocomputing 235, 10-14.

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  • How to Cite

    c, H., & E, L. (2018). Person Recognition by Hilbert Pair of Wavelets using Facial Images. International Journal of Engineering & Technology, 7(3), 1282-1285. https://doi.org/10.14419/ijet.v7i3.11482

    Received date: 2018-04-13

    Accepted date: 2018-05-15

    Published date: 2018-07-04