Face Detecting Using Artificial Neural Network Approach

  • Authors:

  • Shahrin Azuan Nazeer;Nazaruddin Omar;Khairol Faisal Jumari;Marzuki Khalid

  • Affiliations:

  • Telekom Research & Development Sdn Bhd, Malaysia;Telekom Research & Development Sdn Bhd, Malaysia;Universiti Teknologi Malaysia;Universiti Teknologi Malaysia

  • Venue:
  • AMS '07 Proceedings of the First Asia International Conference on Modelling & Simulation
  • Year:
  • 2007

  • Performance improvement of contactless distance sensors using neural network

    IMMURO'12 Proceedings of the 11th WSEAS international conference on Instrumentation, Measurement, Circuits and Systems, and Proceedings of the 12th WSEAS international conference on Robotics, Control and Manufacturing Technology, and Proceedings of the 12th WSEAS international conference on Multimedia Systems & Signal Processing

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Abstract

A frontal face detection system using artificial neural network is presented. The system used integral image for image representation which allows fast computation of the features used. The system also applies the AdaBoost learning algorithm to select a small number of critical visual features from a very large set of potential features. Besides that, it also used cascade of classifiers algorithm which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions. Furthermore, a set of experiments in the domain of face detection is presented. The system yields a promising face detection performance.