Evaluation of Different Features for Face Recognition in Video

Abstract

With man One of the most critical tasks in automated face recognition technology is the extraction of facial features from a facial images. The most critical task in each face recognition (FR) technology, which contributes the most to the success of particular FR products in particular applications and which is highly protected by industries developing those products, is the extraction of facial features from a facial image. This report presents the performance comparison of several publicly reported feature extraction algorithms for face recognitionin video. The evaluated features are Harris corner detection features, FAST (Features from Accelerated Segment Test), GFTT (Good Features To Track), MSER (Maximally Stable Extremal Regions), and HOG (Histograms of Oriented Gradients).

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2014
Accession Number
AD1018011

Entities

People

  • Dmitry O. Gorodnichy
  • Eric Granger
  • Erico Neves
  • Stan Matwin

Organizations

  • Canada Border Services Agency

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Bayesian Networks
  • Computer Science
  • Data Sets
  • Detection
  • Electrical Engineering
  • Engineering
  • Feature Extraction
  • Machine Learning
  • National Security
  • Recognition
  • Security
  • Simulations
  • Test And Evaluation
  • Test Beds
  • Test Sets
  • Video Clips
  • Video Surveillance

Fields of Study

  • Computer science

Readers

  • Computer Vision.

Technology Areas

  • AI & ML