Rotation Invariant Neural Network-Based Face Detection

Abstract

In this paper, we present a neural network-based face detection system. Unlike similar systems which are limited to detecting upright, frontal faces, this system detects faces at any degree of rotation in the image plane. The system employs multiple networks; the first is a router network which processes each input window to determine its orientation and then uses this information to prepare the window for one or more detector networks. We present the training methods for both types of networks. We also perform sensitivity analysis on the networks, and present empirical results on a large test set. Finally, we present preliminary results for detecting faces which are rotated out of the image plane, such as profiles and semi profiles.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1997
Accession Number
ADA341629

Entities

People

  • Henry A. Rowley
  • Shumeet Baluja
  • Takeo Kanade

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Air Platforms
  • Autonomy

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Computer Science
  • Computer Vision
  • Detection
  • Detectors
  • Homosexuality
  • Machine Learning
  • Network Architecture
  • Neural Networks
  • Orientation (Direction)
  • Pattern Recognition
  • Probabilistic Models
  • Recognition
  • Signal Processing
  • Supervised Machine Learning
  • Test Sets

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks