Increased Resolution 3D Face Modeling and Recognition
From Multiple Low Resolution Structure From Motion Models
Chris Boehnen
Patrick Flynn
University of
Notre Dame, chris@boehnen.com
Abstract
We present an approach to
combine multiple noisy low density 3D face models obtained from uncalibrated
video into a higher resolution 3D model. The approach first generates ten 3D
face models (containing a few hundred vertices each) of each subject using 136
frames of video data in which the subject face moves in a range of
approximately 15 degrees from frontal. By aligning, resampling, and merging
these models, we produce a new improved 3D face model containing over 50,000
points. An ICP face matcher employing the entire face achieved a 75% rank one
recognition rate, which falls within the documented range of performance
similar to whole-face 3D matcher results [2] that use more advanced laser
scanners for data acquisition. The simplicity of our hardware requirements
reduces cost, complexity, and may enable the use of “other people’s video” for 3D
face modeling and recognition.
Keywords: Biometrics, 3D Face Detection, Structure From Motion, Uncalibrated Monocular Video, Low Resolution