To prevent moving cast shadows from being misunderstood as part of moving objects in video object segmentation,a novel approach to the shadow detection based on LBP(local binary patterns)is proposed.

In this paper,we present two novel approaches for gender classification by local binary pattern(LBP) based classifiers.

Firstly,this method combines texture features of all sub windows of an image extracted by local binary pattern(LBP) into a local texture feature matrix.

A novel approach to facial expression recognition based on the combination of Local Binary Pattern(LBP)and Support Vector Machine(SVM)is proposed.

After building databases, texture features of endoscopic image are extracted by using local binary pattern (LBP) and trained by support vector machine (SVM).

After research about the human face detection, face alignment, facial expression feature selection, expression classification and PTZ camera controlling, this thesis brings forward a facial expression recognition algorithm based on enhanced local binary pattern with wavelet transform and augmented variance ratio.

The method which combined the characteristic of principal components analysis(PCA) with local binary pattern(LBP)\'s com-bines the advantage in global features of PCA with the advantage in Details of local texture of LBP and could extract better characteristics from face image for support vector machine(SVM) to gender classification.