Face Recognition by Multi-Frame Fusion of Rotating Heads in Video

Shaun Canavan, Michael P. Kozak, Yong Zhang, John R. Sullins, Matthew A. Shreve, and Dmitry B. Goldgof

Date Submitted: Sept. 2007
Date Presented: Sept. 28, 2007
Location: IEEE Conference on Biometrics: Theory and Applications and Systems on Sept. 27th in Washington DC.

ABSTRACT

This paper presents a face recognition study that implicitly utilizes the 3D information in 2D video sequences through multi-sample fusion. The approach is based on the hypothesis that continuous and coherent intensity variations in video frames caused by a rotating head can provide information similar to that of explicit shapes or range images. The fusion was done on the image level to prevent information loss. Experiments were carried out using a data set of over 100 subjects and promising results have been obtained:
  1. under regular indoor lighting conditions, rank one recognition rate increased from 91% using a single frame to 100% using 7-frame fusion
  2. under strong shadow conditions, rank one recognition rate increased from 63% using single frame to 85% using 7-frame fusion.