摘要
人脸识别是计算机视觉和图像模式识别领域的一个重要技术。主成分分析(PCA)是人脸图像特征提取的一个重要算法。而支持向量机(SVM)有适合处理小样本问题、高维数及泛化性能强等多方面的优点。文章将两者结合,先用PCA算法进行人脸图像特征提取,再用SVM进行分类识别。通过基于ORL人脸数据库的计算机仿真实验表明,该方法具有很好的可行性和实际意义。
Face recognition technoloyg is an important technology in the computer vision and image pattern recognition field. Pirncipal component analysis（PCA） is an impotrant algoirthm in face feature extraction. According to the high per formanee of support vector machine（SVM） in tackling small sample size, high-dimension and its good generalization. PCA is combined with SVM algoirthm in this paper. After etracting the feature from face image by using the PCA, it use the SVM to classify face. The computer simulating experiment based on ORL face database shows the meathod is feasible.
出处
《计算机与数字工程》
2011年第7期124-126,143,共4页
Computer & Digital Engineering
