Robust Face Detection from Still Images

Abstract

Facial recognition is one of the most studied topics in the field of biometrics because of its varied applications. Detection of dark colored faces and poorly illuminated faces are not well studied in the literature due to several challenges. The most critical challenge is that there is inadequate contrast among facial features. To overcome this challenge, a new face detection methodology, which consists of histogram analysis, Haar wavelet transformation and Adaboost learning techniques, is proposed. The extended Yale Face Database B is used to examine the performance of the proposed method and compared against commonly used OpenCVs Haar detection algorithm. The experimental results with 9,883 positive images and 10,349 negative images showed a considerable improvement in face hit rates without a significant change in false acceptance rates.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2014
Accession Number
AD1018028

Entities

People

  • Chrisford Ling
  • Patrick Laytner
  • Qinghan Xiao

Organizations

  • Defence Research and Development Canada

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Acceptance Tests
  • Algorithms
  • Brightness
  • Detection
  • Detectors
  • Electronic Warfare
  • Engineering
  • Equalization
  • Exponential Functions
  • Feature Selection
  • Gray Scale
  • Histograms
  • Illumination
  • Integrals
  • Intensity
  • Machine Learning

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Regression Analysis.