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Support Vector Machine (SVM), as a classical nonlinear classifier, is used for pattern recognition. It can map the training samples from the low dimensional space of never linear classification to the high dimensional space of the linear classification, and then do the classification. This paper mainly trains the classifier to make it learn to distinguish the face and the non face. The support vector machine has complete mathematical derivation, rigorous algorithm logic, and more complex than Adaboost on the whole, but it has a good effect in the case of less sample size, so there is a sample advantage. The theory that supports it includes generalization theory, optimization theory and kernel function, which are also widely used in other machine learning algorithms, such as neural networks, which have been proved to have high reliability for several decades. At the same time, this paper discusses the principal component analysis (PCA) technology to compress data, realize data reduction, and provide great help to the algorithm of data preprocessing, which greatly reduces the dimension of input data of SVM support vector machine, and greatly improves the operation and detection time.