1 Support Vector Machines via a decision boundary optimization, has proved is an excellent classifier in the model recognition.

2 The principle of finding optimized decision boundary give SVM excellent performance on linear separatable problems.

3 A new algorithm based on fuzzy inference theory is proposed for generating decision rules of pattern classification, which can define a complex nonlinear decision boundary.

4 Among the existed methods, one kind of these methods is based on the decision boundary.

5 Motivated by reducing the training patterns, a grid-based algorithm is proposed to pre-select a subset of patterns near the decision boundary.

6 As a result, for images with complex backgrounds, a fixed decision boundary skin-color model may lead to high false rejection rate and false detection rate.