A Novel Two-Layer PCA/MDA Scheme for Hand Posture Recognition

Authors:
Jiang-Wen Deng;H. T. Tsui
Affiliations:
-;-
Venue:
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Year:
2002

Citing 0
Cited 7

Automatic Sign Language Analysis: A Survey and the Future beyond Lexical Meaning

IEEE Transactions on Pattern Analysis and Machine Intelligence
Signing Exact English (SEE): Modeling and recognition

Pattern Recognition
Signing Exact English (SEE): Modeling and recognition

Pattern Recognition
Feature alignment approach for hand posture recognition based on curvature scale space

Neurocomputing
A person independent system for recognition of hand postures used in sign language

Pattern Recognition Letters
Tongue-print: a novel biometrics pattern

ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Dynamic affine-invariant shape-appearance handshape features and classification in sign language videos

The Journal of Machine Learning Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

Principle Component Analysis (PCA) and Multiple Discriminant Analysis (MDA) have long been used for the appearance-based hand posture recognition. In this paper, we propose a novel PCA/MDA scheme for hand posture recognition. Unlike other PCA/MDA schemes, the PCA layer acts as a crude classification. Since posture alone cannot provide sufficient discriminating information, each input pattern will be given a likelihood of being in the nodes of PCA layers, instead of a strict division. Based on the Expectation-Maximization (EM) algorithm, we introduce three methods to estimate the parameters for this crude classification during training. The experiments on a 110-sign vocabulary show a significant improvement compared with the global PCA/MDA.