作者:关小蕊， 程卫月， 张雪琴， 林克正， 高铁洪
Authors:GUAN Xiao-rui, CHENG Wei-yue, ZHANG Xue-qin, LIN Ke-zheng, GAO Tie-hong摘要:摘 要:针对传统Gabor小波变换提取的特征向量维数较高以及DBN在完成人脸识别时会忽略局部信息的问题，提出了一种基于GCSLBP的DBN人脸识别算法(Gabor fusion central symmetric local binary pattern deep belief network, GCSLBPDBN )。该算法首先改进了原始的Gabor变换，通过引入中心对称局部二值模式方法（local binary pattern, LBP）进行优化，然后利用直方图的方法表示最终的特征向量，既提取到图像丰富的局部特征，又能降低特征向量维数。最后使用深度信念网络方法提高分类鲁棒性，完成人脸的分类和识别。该算法已在ORL和CMU_PIE数据集上进行仿真实验，实验结果表明，本文GCSLBP-DBN 算法有效的提高了人脸识别率，在光照等变换下也具有鲁棒性。
Abstract:Abstract:Aiming at the problem that the feature vector extracted by original Gabor transform has a high dimension and DBN ignores local information when completing face recognition, a DBN face recognition algorithm based on GCSLBP is proposed. Firstly, the algorithm improves the original Gabor transform, and optimizes it by introducing the LBP . Then the algorithm uses the histogram method to represent the final feature vector, which extracts the rich local features of the image and reduces the dimensionality of the feature vector. Finally, DBN is used to improve the robustness of classification and complete the classification and recognition of faces. The algorithm has been simulated experiments on ORL and CMU_PIE data sets. The experimental results show that the algorithm in this paper effectively improves the face recognition rate and are also robust under light and other transformations.
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基于GCSLBP的DBN人脸识别算法
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