Face Recognition Using Multispectral Random Field Texture Models, Color Content, and Biometric Features

  • Authors:

  • Orlando J. Hernandez;Mitchell S. Kleiman

  • Affiliations:

  • The College of New Jersey;The College of New Jersey

  • Venue:
  • AIPR '05 Proceedings of the 34th Applied Imagery and Pattern Recognition Workshop
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

Abstract

Most of the available research on face recognition has been performed using gray scale imagery. This paper presents a novel two-pass face recognition system that uses a Multispectral Random Field Texture Model, specifically the Multispectral Simultaneous Auto Regressive (MSAR) model, and illumination invariant color features. During the first pass, the system detects and segments a face from the background of a color image, and confirms the detection based on a statistically modeled skin pixel map and the elliptical nature of human faces. In the second pass, the face regions are located using the same image segmentation approach on a subspace of the original image, biometric information, and spatial relationships. The determined facial features are then assigned biometric values based on anthropometrics, and a set of vectors is created to determine similarity in the facial feature space.