Fast Method for Face Detection Based on the Characteristic of Cascade Classifier

Abstract

We propose a fast method of face detection that is based on the characteristics of a cascade classifier that is called a cascade step search (CSS). The proposed method has two features. First is a gradual classification that uses only a few layers of the cascade classifier to estimate the face-likelihood distribution. Second is an efficient search that uses the face likelihood distribution. The search is operated at intervals that are optimally changed according to the likelihood. This reduces the number of sub-windows that must be processed. The face-likelihood on the image window exposes the likelihood near the window and the next scaled one. These features can reduce the costs of classifications in the face detection process. Our experiments on face detection show that the proposed method is about five times faster than the traditional searches and maintains a high detection rate.