Topology modeling for Adaboost-cascade based object detection

Several important issues involved in Adaboost-cascade learning still remain open problems. In this work, several novel ideas are proposed for improved Adaboost-cascade object detection. The most important one is the novel topology oriented Adaboost (TOBoost) algorithm. TOBoost immediately minimizes the classification error of each selected feature, and thus enables the final detector to be more discriminative and to converge more quickly. Moreover, a simple cascading scheme is presented for tuning the cascade parameters of TOBoost; and Gaussian kernel density estimation is introduced to enhance the generalization ability of TOBoost. Another important contribution is the topology modeling of Haar-like (HL) features, which reveals an interesting property of negative HL features and significantly avoids unnecessary training computations. Non-adjacent Haar-like features are consequently configured for more effective object representation. The above enhancements result in a more efficient and stable detector with fewer features. Extensive experiments in the application of iris detection are conducted and encouraging performance is achieved.