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(陕西省信息获取与处理重点实验室,西北工业大学电子信息学院, 西安 710072)

摘 要
人脸识别是当前人工智能和模式识别的研究热点。基于对小波分解和局部二进制模式(LBP)分析,提出了一种多级LBP直方图的序列特征 (M-HSLBP) 的提取方法。2维的小波分解具有对表情变化不敏感的特点,可以很好地压缩和表征人脸图像的特征;LBP是一种有效的纹理描述算子,使用多级可变大小的子窗口对小波变换后的图像进行扫描,对各级子图像进行改进LBP变换并形成多级LBP直方图序列特征,这种特征既能反映人脸局部特征又能反映其整体特征。径向基网络作为分类器具有很高的推广性能,有利于大容量样本的分类。在对人脸库ORL和YEL的识别实验中,该算法识别率达到98%以上,与传统算法相比,取得了更好的识别结果。
Face Recognition Using Multi-level Histogram Sequence Local Binary Pattern

GAO Tao1,2, HE Mingyi1,2, DAI Yuchao1,2, BAI Lin1,2(1.Key Laboratory of Information Acquisition and Processing;2.Electronic and Information School, Northwestern Polytechnical University, Xian 71007)

Face recognition is an active research area in the artificial intelligence. A face recognition algorithm using the RBF network is proposed based on wavelet analysis and multi-level histogram sequence local Binary pattern (M-HSLBP). Since wavelet analysis is insensitive to changes in expression, it can express the principal features of the face image by compressing data. LBP is an efficient local texture description operator. The wavelet transformed images were scanned with multi-degree changeable Sub-windows. Sub-images were transformed by an enhanced LBP, and then the LBP features are concatenated into an enhanced feature vector, which can express both local and holistic features of the face image. RBF network with high generalization is a good classifier, especially for larger number of samples. Experimental results on ORL and YALE face show that the proposed algorithm, which achieves recognition accuracy of above 98% is more effective and faster than the traditional method.