Training more discriminative multi-class classifiers for hand detection

In this paper, an effective algorithm is developed to learn more discriminative multi-class classifiers for achieving more accurate hand detection. At each round of boosting, a set of shared stump classifiers with relatively low discrimination power are selected by using a "slowest error growth" discriminant, and they are further combined to generate a multi-class classifier with high discrimination power. For the learned multi-class classifier, all of its shared stump classifiers can jointly cover all the potential situations (i.e., various classes of hand postures) sufficiently and discriminate each class of hand postures more effectively. In addition, multiple thresholds are set for each stump classifier to enhance its discrimination power. Finally, the optional mask images are further used to reduce both the feature dimensions and the computational cost for searching the appropriate features. The experimental results on both our hand dataset and NUS hand posture dataset-II have demonstrated the effectiveness and efficiency of our algorithm. HighlightsA set of shared stumps are combined to strengthen the discrimination power of weak classifiers.A "slowest error growth" discriminant to determine the optimal combination of stumps.Multiple thresholds are leveraged in shared stumps to fit different classes.We associate effective features with different classes of hands, and employ mix-type features.As compared with JointBoost, our classifier can obtain a better classification performance with less runtime cost.