Generalized BRIEF: A Novel Fast Feature Extraction Method for Robust Hand Detection

Most literatures have been relying on image processing approaches such as skin detection and depth thresholding for hand detection. These techniques are restricted by strong assumptions and normally possess low robustness in actual applications. In this paper, we focus on an appearance approach and propose a new feature extraction method based on sparse pixel-pair wise intensity comparisons for hand detection. Our method can be viewed as a generalized BRIEF descriptor and can be easily adopted for other object detection or recognition tasks. We perform extensive experiments and prove that our method achieves comparable results with normal, noisy, and occluded hand images in term of both test accuracy and ROC. The main contributions of our work are threefold: 1) We introduce a new and simple feature extraction method that is robust against image noise, cluttered backgrounds, and partial occlusion. 2) Combined with AdaBoost, we show that the new feature descriptor is effective for hand detection. 3) The new feature descriptor has been rigorously compared with existing feature descriptors with a new hand database that has very challenging image backgrounds.