IEEE Transactions on Pattern Analysis and Machine Intelligence
A System for Person-Independent Hand Posture Recognition against Complex Backgrounds
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Detection in Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust classification of hand postures against complex backgrounds
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
WACV '96 Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision (WACV '96)
Skin Detection in Video under Changing Illumination Conditions
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Adaptive color space switching based approach for face tracking
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Handy: A real-time three color glove-based gesture recognizer with learning vector quantization
Expert Systems with Applications: An International Journal
Hand posture classification by means of a new contour signature
ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
Hi-index | 12.05 |
In this paper, we present a novel approach for hand gesture recognition based on Gabor filters and support vector machine (SVM) classifiers for environments with varying illumination. The proposed method (1) is robust against varying illumination, which is achieved using an adaptive skin-color model switching method; (2) is insensitive to hand-pose variations, which is achieved using a Gabor filter-based gesture angle estimation and correction method; (3) allows users to wear either a long- or short-sleeve shirt, which is achieved using a method that segments the hand from the forearm. To evaluate the robustness of the proposed method, we created a database of hand gestures in realistic conditions. A recognition rate of 96.1% was achieved using the proposed method. A dynamic gesture recognition system is also presented for real-life conditions. In the proposed system, the recognition results improved from 72.8% to 93.7% when the hand-pose correction module was used, indicating that using the responses of Gabor filters to estimate the hand-pose angle is effective.