Multi-View Face Detection Based on Multi-Features AdaBoost Collaborative Learning Algorithm

Abstract

In View of the Multi-View Face Detection Problem under Complex Background, an Improved Face Detection Method Based on Multi-Features Boosting Collaborative Learning Algorithm Integrating Local Binary Pattern (LBP) is Presented. Firstly, Facial Skin Color Information is Used to Exclude most of the Background Regions. then, Haar-like Feature and LBP Feature are Extracted from Facial Candidate Regions and Inputted into a Modified Adaboost Algorithm to Obtain a Strong Classifier. Lastly, in Order to Improve the Detection Speed, Pyramid Classifier System Structure is Adopted to Determine the Face. the Experimental Results on CMU Standard Test Set and Life Photos, the Proposed Method has Achieved the Rapid Detection of Multi-View Face Image.