基于Gabor滤波特征和支持向量机的人脸检测

人脸检测是人脸识别与图像及视频检索的一项重要任务。论文提出了一种基于Gabor滤波特征和支持向量机的正面人脸检测方法。算法首先利用了Gabor滤波器的良好的空间位置与方向的选择特性,采用了四种方向的Gabor滤波器提取人脸样本图像特征并用PCA方法对特征降维,然后用已降维的特征训练支持向量机分类器。最后应用SVM分类检测人脸。实验结果证明该方法行是十分有效的。

Face detection based-on gabor filter and support vector machines

Face detection is an important task in face recognition and content-based image and video retrieval.A method of frontal face detection based-on feature extract using Gabor filter features and support vector machines is proposed in this paper. Take advantages of the desirable characteristics of spatial locality and orientation selectivity of Gabor filters,the paper designs four filters corresponding to four orientation for extracting features from face images.After extracting the features,a reduced feature subspace is learned by principal component analysis.The feature vectors based on Gabor fliers is used as the input of support vector machines to he trained and classified.A test image is detected by the trained support vector machines.The experiment results show the method can effectively detect face.