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首页> 外文期刊>International Journal of Intelligent Computing and Cybernetics >A novel face recognition in uncontrolled environment based on block 2D-CS-LBP features and deep residual network
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A novel face recognition in uncontrolled environment based on block 2D-CS-LBP features and deep residual network

机译：基于块2D-CS-LBP特征和深度剩余网络的不受控制环境中的小说面识别

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摘要

Purpose - In order to solve the problem that the performance of the existing local feature descriptors in uncontrolled environment is greatly affected by illumination, background, occlusion and other factors, we propose a novel face recognition algorithm in uncontrolled environment which combines the block central symmetry local binary pattern (CS-LBP) and deep residual network (DRN) model. Design/methodology/approach - The algorithm first extracts the block CSP-LBP features of the face image, then incorporates the extracted features into the DRN model, and gives the face recognition results by using a well-trained DRN model. The features obtained by the proposed algorithm have the characteristics of both local texture features and deep features that robust to illumination. Findings - Compared with the direct usage of the original image, the usage of local texture features of the image as the input of DRN model significantly improves the computation efficiency. Experimental results on the face datasets of FERET, YALE-B and CMU-PIE have shown that the recognition rate of the proposed algorithm is significantly higher than that of other compared algorithms. Originality/value - The proposed algorithm fundamentally solves the problem of face identity recognition in uncontrolled environment, and it is particularly robust to the change of illumination, which proves its superiority.
机译：目的 - 为了解决不受控制的环境中现有本地特征描述符的性能的问题，受到照明，背景，遮挡等因素的大大影响，我们提出了一种新的面部识别算法在不受控制的环境中，结合了块中央对称本地二进制模式（CS-LBP）和深度剩余网络（DRN）模型。设计/方法/方法 - 算法首先提取面部图像的块CSP-LBP特征，然后将提取的特征结合到DRN模型中，并通过使用训练有素的DRN模型来提供面部识别结果。由所提出的算法获得的特征具有局部纹理特征和鲁棒照明的深度特征的特征。调查结果 - 与原始图像的直接使用相比，作为DRN模型的输入的图像的局部纹理特征的用法显着提高了计算效率。 Feret的面部数据集的实验结果表明，所提出的算法的识别率明显高于其他比较算法的识别率。原创性/值 - 所提出的算法从根本上解决了不受控制的环境中面部身份识别的问题，对照明的变化特别强大，这证明了其优越性。

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