The Research of Multi-angle Face Detection Based on Multi-feature Fusion

Abstract

The method of multi-angle face detection method is proposed based on fusion of Haar-like, HOG and MB-LBP features. Firstly, three Adaboost cascade classifiers for original face region detection are constructed respectively according to Haar-like, MB-LBP and HOG features, using the processed training samples of face and non-face to train the classifiers. Secondly, the preprocessing of testing sample is implemented based on skin color model, which results are the classifiers input, and then the suspected face regions and their weights are obtained. Finally, the refined face regions are selected according to the results of voting and weighted threshold. The method proposed in this paper is implemented on VS2012 platform invoking opencv function library, and the simulation experiment is carried on FDDB. To verify the effect, our method is compared with other methods based on a single feature. Results of experiments show that the proposed method has higher accuracy and better real-time performance.