A Two-level Pose Estimation Framework Using Majority Voting of Gabor Wavelets and Bunch Graph Analysis
Junwen Wu, Jens Myrup Pedersen, Duangmanee Putthividhya, Daniel NØRGAARD, Mohan M. Trivedi · 2008
In this paper a two-level approach for estimating face pose from a single static image is presented. Gabor wavelets are used as the basic features. The objective of the first level is to derive a good estimate of the pose within some uncertainty. The objective of the second level processing is to minimize this uncertainty by analyzing finer structural details captured by the bunch graphs. The first level analysis enables the use of rigid bunch graph. The framework is evaluated with extensive series of experiments. Using only a single level, 90 % accuracy (within ±15 degree) and over 98 % (within ±30 degree) was achieved on the complete dataset of 1,395 images. Second level classification was evaluated for three sets of poses with accuracies ranging between 67-73%, without any uncertainty.