Comparative Performance Evaluation of Gray-Scale and Color Information for Face Recognition Tasks

Authors:
Srinivas Gutta;Jeffrey Huang;Chengjun Liu;Harry Wechsler
Affiliations:
-;-;-;-
Venue:
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Year:
2001

Citing 3
Cited 2

Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces

IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic recognition and analysis of human faces and facial expressions: a survey

Pattern Recognition
C4.5: programs for machine learning

C4.5: programs for machine learning

Bayesian Networks to Combine Intensity and Color Information in Face Recognition

ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
A comparative study of preprocessing mismatch effects in color image based face recognition

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper assesses the usefulness of color information for face recognition tasks. Experimental results using the FERET database show that color information improves performance for detecting and locating eyes and faces, respectively, and that there is no significant difference in recognition accuracy between full color and gray-scale face imagery. Our experiments have also shown that the eigenvectors generated by the red channel lead to improved performance against the eigenvectors generated from all the other monochromatic channels. The probable reason for this observation is that in the near infrared portion of the electro-magnetic spectrum, the face is least sensitive to changes in illumination. As a consequence it seems that the color space as a whole does not improve performance on face recognition but that when one considers monochrome channels on their own the red channel could benefit both learning the eigenspace and serving as input to project on it.