Three-dimensional facial feature points matching based on a combined support vector machine

Authors:
Hongjing Ma;Detong Zhang;Jun Feng
Affiliations:
Northwest University;Northwest University;Northwest University
Venue:
Proceedings of the First International Conference on Internet Multimedia Computing and Service
Year:
2009

Citing 5
Cited 0

Local Invariants For Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining local features for robust nose location in 3D facial data

Pattern Recognition Letters
Robust point correspondence matching and similarity measuring for 3D models by relative angle-context distributions

Image and Vision Computing
A new framework for feature descriptor based on SIFT

Pattern Recognition Letters
Chi-square goodness-of-fit test of 3d point correspondence for model similarity measure and analysis

CIVR'05 Proceedings of the 4th international conference on Image and Video Retrieval

Quantified Score

Hi-index	0.00

Visualization

Abstract

Feature points searching or point correspondence matching is a challenge problem in computer vision and pattern recognition, which is very important perquisite for many applications such as image registration, object recognition and statistical model construction. In this paper, we propose an algorithm for facial feature points matching. Specifically, the candidate pre-matching sets are first selected for each feature points based on our previously proposed algorithm called relative angle --context distributions (RACD). Afterwards, Supported Vector Machine based classification is employed for final accurate corresponded location. The experimental results demonstrate that our algorithm achieves very good performance for most of the facial feature points.