Human Face Recognition by Using Bit-plane Disintegration

In recent years face recognition has received substantial attention from researchers, but still remained very challenging in real applications. A lot of face recognition algorithms, along with their modifications, have been developed during the past decades. Although many face recognition techniques have been proposed and many achievements have been obtained, we can’t get high recognition rate due to the changes of pose, illumination, and expression. In this paper, human face recognition is studied related to ensemble methods.In order to improve variety of component classifiers, the idea of bit-plane disintegration is used and moving window classifier is used as a basic individual classifier. The quantized pattern representations’ layers are used jointly to make a decision. In this study, several fused methods such as sum, product, majority vote, max, min, and median rules are used. Experiments results with face images databases show that fusion of multiple classifiers has good classification performance. Moreover, we compare different multiple classifier schemes with other human face recognition techniques.