摘要
对贝叶斯分类中最大似然(ML)公式进行了简化,给出了一种实用的快速计算相似度的方法,在此基础上设计了基于分块Gabor特征提取的贝叶斯人脸识别算法。该算法从原始数字图像出发,先对图像矩阵进行分块,然后对分块子图像进行多分辨率的Gabor特征提取,对每一个特征块设计一个贝叶斯分类器,通过将这些分类器加权平均,得到最后的决策。在FERET人脸数据库的实验结果验证了该方法的有效性。
An improved Maximum Likelihood（ML） measure is proposed, which simplifies the similarity computation in the Bayesian algorithm. And then a novel block-based Gabor transformed for Bayesian face recognition is proposed. The original sample images are divided into smaller sub-images, utilizing the convolution of the sub-images and the Gabor filters to extract features, each sub-image is designed as a ML classifier of Bayesian, by use of weighed average similarity to make the final deci- sion. The experiments on FERET face database have shown the effectiveness of the method.
出处
《计算机工程与应用》
CSCD
2013年第14期199-202,共4页
Computer Engineering and Applications
基金
河南省科技攻关计划项目(No.122102210505
No.12A520026)
河南省基础与前沿技术研究计划项目(No.122300410111)
