Ear detection based on improved AdaBoost algorithm

In this paper, we propose the ear detection approach under complex background which has two stages: off-line cascaded classifier training and on-line ear detection. In the stage of off-line training, considering the unique contour, the concave and convex of the ear, we apply the extended haar-like features to construct the space of the weak classifiers using the nearest neighbor norms. And then we choose the gentle AdaBoost algorithm to train the strong classifiers which form the cascaded multi-layer ear detector. In the stage of on-line detection, we apply two methods to speed up the detection procedure. The first one is to adjust the threshold of the strong classifiers to remain the like-ear sub windows for further processing only using the first two layer classifiers. The second one is to keep the size of the original image while scaling the detection sub-windows to locate the ear part. The ear detection experiments on USTB ear database, CAS-PEAL face database and CMU PIE database show that the proposed method is significantly efficient and robust.