Real-time face detection technology can be applied in many industrial or commercial products. Many face detection applications use the traditional Adaboost face detection system which is proposed by Viola and Jones in 2004. Viola and Jones used the Adaboost training algorithm and the Haar-like features in their proposed traditional Adaboost face detection system, which has a high detection rate...

This paper presents a method of detecting faces based on Cost-Sensitive AdaBoost (CS-AdaBoost) algorithm. The two main differences between CS-AdaBoost algorithm and the naïve AdaBoost are that (1) unequal initial weights are given to each training sample according to its misclassification cost, and (2) the weights are updated separately for positives and negatives at each boosting step. Due to ...

Stability has been explored to study the performance of learning algorithms in recent years and it has been shown that stability is sufficient for generalization and is sufficient and necessary for consistency of ERM in the general learning setting. Previous studies showed that AdaBoost has almost-everywhere uniform stability if the base learner has L1 stability. The L1 stability, however, is t...

In order to study the convergence properties of the AdaBoost algorithm, we reduce AdaBoost to a nonlinear iterated map and study the evolution of its weight vectors. This dynamical systems approach allows us to understand AdaBoost’s convergence properties completely in certain cases; for these cases we find stable cycles, allowing us to explicitly solve for AdaBoost’s output. Using this unusual...

In this report, the AdaBoost algorithm is applied to multi-class 3D gesture recognition problem. The performance of AdaBoost is compared across different base classifiers and between different data sets. One method of improving AdaBoost by regularizing the distribution weights is also presented and discussed.

In order to solve the overfitting of sample weights and the low detection rate in training process of the traditional AdaBoost algorithm, an improved AdaBoost algorithm based on Haar-like features and LBP features is proposed. This method improves weight updating rule and weights normalization rule of the traditional AdaBoost algorithm. Then combining this method with the AdaBoost algorithm bas...