The application of extended geodesic distance in head poses estimation

Authors:
Bingpeng Ma;Fei Yang;Wen Gao;Baochang Zhang
Affiliations:
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin, China
Venue:
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Year:
2006

Citing 4
Cited 1

Support-Vector Networks

Machine Learning
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms

IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces for recognition

Journal of Cognitive Neuroscience

Head pose estimation using simple local gabor binary pattern

CCBR'11 Proceedings of the 6th Chinese conference on Biometric recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper we proposes an extended geodesic distance for head pose estimation. In ISOMAP, two approaches are applied for neighborhood construction, called k-neighbor and ε-neighbor. For the k-neighbor, the number of the neighbors is a const k. For the other one, all the distances between the neighbors is less than ε. Either the k-neighbor or the ε-neighbor neglects the difference of each point. This paper proposes an new method called the kc-neighbor, in which the neighbors are defined based on c time distance of the k nearest neighbor, which can avoid the neighborhood graph unconnected and improve the accuracy in computing neighbors. In this paper, SVM rather than MDS is applied to classify head poses after the geodesic distances are computed. The experiments show the effectiveness of the proposed method.