Performance Evaluation for 5G NR Based MM Wave MIMO Systems

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The detection of human face from images plays a vital role in Computer vision, cognitive science and Forensic Science. The various computational and mathematical models, for classifying face including Scale Invariant Feature transform (SIFT) and Dominant Rotated Local Binary Pattern (DRLBP) have been proposed yields better performance. This paper proposes a novel method of classifying the human face using Haar . This is done by pre-processing the face image at first and then extracting the face features. Then the detection of human faces is done using Haar.


  • Appearance based methods involves LDA
  • Geometric methods.


  • In appearance based methods, less accurate of features description because of whole image consideration
  • In geometric based methods, the geometric features like distance between eyes, face length and width, etc., are considered which not provides optimal results


  • Neural networks
  • Feature Extraction
  • Haar cascades classifier



  • Preprocessing and Normalization
  • Haar cascades classifier
  • Neural networks


  • Detecting accuracy is high due to extracting local features of the image
  • The geometric features like distance between eyes, face length and width, etc., are considered which provides high optimal results


  • Queue forming
  • People counting




  1. Ahonen.T, Hadid.A and Pietik?inen.M, (2004) ?Face description with local binary patterns?, in Proc. Eur. Conf. Comput. Vis.
  2. Anbang Yao and Shan Yu, (2013) ?Robust Face Representation Using Hybrid Spatial Feature Interdependence Matrix?.
  3. Daubechies.I, (1990) ?Thewavelet transform time-frequency localization and signal analysis?, IEEE Trans. Information Theory.
  4. Jain.A.K,(1989) ?Fundamentals of digital image processing?, Prentice Hall.
  5. JulienMeynet,(16th July 2003)?Fast Face Detection Using AdaBoost?.

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