Face Detection Based Successive Mean Quantization Transform
Xiao Ling-li · Video Engineering · 2013
To improve the problems of accuracy and robustness under the circumstances of rotate,covering and illumination in face detection,a method is presented which uses the Successive Mean Quantization Transform(SMQT).Firstly,local face feature of candidate regional is extracted using SMQT.Then,the face feature is learned to train the SNoW classifier.Finally,the purpose of positioning faces is achieved accurately through classify face and face samples with the SNoW.Empirical results show the method outperforms than neural networks,support vector machines and Bayesian methods in accuracy and robustness even though with complex background,more than one faces,covering and illumination effects situations.