Higher-Order Local Autocorrelation Feature Extraction Methodology for Hand Gestures Recognition
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Date
2017-12-25
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Abstract
A novel feature extraction method for hand gesture recognition from sequences of image frames is described and tested. The proposed method employs higher order local autocorrelation (HLAC) features for feature extraction. The features are extracted using different masks from Grey-scale images for characterising hands image texture with respect to the possible position, and the product of the pixels marked in white. Then features with the most useful information are selected based on mutual information quotient (MIQ). Multiple linear discriminant analysis (LDA) classifier is adopted to classify different hand gestures. Experiments on the NUS dataset illustrate that the HLAC is efficient for hand gesture recognition compared with other feature extraction methods.
Description
Keywords
Feature extraction , Gesture recognition , Mutual information , Correlation , Training , Redundancy , Classification algorithms
Citation
IEEE