Fast and Fully Automatic Ear Detection Using Cascaded AdaBoost

Abstract

Ear detection from a profile face image is an important step in many applications including biometric recognition. But accurate and rapid detection of the ear for real-time applications is a challenging task, particularly in the presence of occlusions. In this work, a cascaded AdaBoost based ear detection approach is proposed. In an experiment with a test set of 203 profile face images, all the ears were accurately detected by the proposed detector with a very low (5 x 10-6) false positive rate. It is also very fast and relatively robust to the presence of occlusions and degradation of the ear images (e.g. motion blur). The detection process is fully automatic and does not require any manual intervention.