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Local Binary Patterns for Face Recognition Under Varying Variations

机译:不同变化下的面部识别的局部二进制模式

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In this paper, an integrated method of Local Binary Pattern (LBP) and Discrete Cosine Transform (DCT) is used for face recognition under different variations with illumination, pose and facial expression. LBP is applied as a preprocessing step to generate the LBP image which is then analyzed on a block-by-block basis. Facial features are extracted from each block using the DCT algorithm. The objective is to explore the performance of LBP under different illumination, pose and expression variations. Face images in the Yale database and ORL database are used in our experiments. A comparison on the sensitivities of the LBP operator in illumination, pose and expression variations is provided.
机译:在本文中,局部二进制图案(LBP)和离散余弦变换(DCT)的集成方法用于不同变化的面部识别,具有照明,姿势和面部表情。将LBP应用为预处理步骤以生成LBP图像,然后以逐块为基础分析。使用DCT算法从每个块中提取面部特征。目的是探讨不同照明,姿势和表达变化下LBP的性能。在我们的实验中使用了Yale数据库和ORL数据库中的面部图像。提供了对照明,姿势和表达变化的LBP操作员的敏感性的比较。

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