Hand Gesture Recognition Using Adaboost with SIFT
Date Issued
2007
Date
2007
Author(s)
Wang, Ko-Chih
DOI
en-US
Abstract
EXISTING hand detection approaches based on the Viola-Jones’ methods have two fundamental issues, background noise of training images could generate poor performance and rotation-variant. As hands are non-rigid objects, positive training images often contain many other objects which degrade the training performance in Adaboost dramatically. Existing approaches often involve a great deal of manual labeling and a highly computational cost. Although the approaches based on the Viola-Jones’ methods could achieves rotation-invariant in a way of treating the problem as a multi-class classification problem, the process would need more training images and lose training and detection performance. We propose a rotation-invariant hand detector using discrete Adaboost with Lowe’s SIFT keypoint detector, which solves the addressed problems simultaneously. Minimal effort is needed for labeling training data and the performance is maintained. As SIFT keypoints are invariant to translation, scaling and rotation, and are minimally affected by small background noise, the proposed approach achieve rotation-invariant detection straightforwardly. How to create the multi-gesture classification
systemis the next step after the single gesture detector is trained. Sequentially executes the single gesture detectors is a general approach to classify multi-gesture. However this
approach increases the recognition time as the number of gestures. Classifying different gestures is harder than only classifying a gesture and background. We use sharing features
to classify the image is a hand gesture or not. Non-sharing feature can point out the diversity of between gestures and achieve better recognition result. The experiment show a
better training and recognition speed and accuracy compared to other existing approaches.
Subjects
圖形辨識
手勢辨識
多類別分類
pattern recognition
hand gesture recognition
multi-class classification
Type
thesis
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