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LBP based on multi wavelet sub-bands feature extraction used for face recognition

机译:基于多小波子带特征提取的LBP用于人脸识别

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摘要

The strategy of extracting discriminant features from a face image is immensely important to accurate face recognition. This paper proposes a feature extraction algorithm based on wavelets and local binary patterns (LBPs). The proposed method decomposes a face image into multiple sub-bands of frequencies using wavelet transform. Each sub-band in the wavelet domain is divided into non-overlapping sub-regions. Then LBP histograms based on the traditional 8-neighbour sampling points are extracted from the approximation sub-band, whilst 4-neighbour sampling points are used to extract LBPHs from detail sub-bands. Finally, all LBPHs are concatenated into a single feature histogram to effectively represent the face image. Euclidean distance is used to measure the similarity of different feature histograms and the final recognition is performed by the nearest-neighbour classifier. The above strategy was tested on two publicly available face databases (Yale and ORL) using different scenarios and different combination of sub-bands. Results show that the proposed method outperforms the traditional LBP based features.
机译:从面部图像中提取判别特征的策略对准确的面部识别极为重要。提出了一种基于小波和局部二值模式(LBP)的特征提取算法。所提出的方法使用小波变换将面部图像分解为多个频率子带。小波域中的每个子带被划分为不重叠的子区域。然后从近似子带中提取基于传统8邻点采样点的LBP直方图,而4邻点采样点则用于从细节子带中提取LBPH。最后,将所有LBPH连接到单个特征直方图中,以有效地表示人脸图像。欧几里得距离用于测量不同特征直方图的相似性,最终识别由最近邻分类器执行。在不同的场景和不同的子波段组合下,在两个公开的人脸数据库(Yale和ORL)上测试了上述策略。结果表明,该方法优于传统的基于LBP的特征。

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