Component-Based Face Detection

Abstract

We present a component-based, trainable system for detecting frontal and near-frontal views of faces in still gray images. The system consists of a two-level hierarchy of Support Vector Machine (SVM) classifiers. On the first level, component classifiers independently detect components of a face. On the second level, a single classifier checks if the geometrical configuration of the detected components in the image matches a geometrical model of a face. We propose a method for automatically learning components by using 3-D head models. This approach has the advantage that no manual interaction is required for choosing and extracting components. Experiments show that the component-based system is significantly more robust against rotations in depth than a comparable system trained on whole face patterns.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2001
Accession Number
ADA457993

Entities

People

  • Bernd Heisele
  • Massimiliano Pontil
  • Thomas Serre
  • Tomaso Poggio

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence Software
  • Bayesian Networks
  • Detection
  • Detectors
  • Information Science
  • Kernel Functions
  • Learning
  • Machine Learning
  • Models
  • Neural Networks
  • Pattern Recognition
  • Polynomials
  • Probability
  • Recognition
  • Rotation
  • Supervised Machine Learning
  • Three Dimensional

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML