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A Face Authentication Scheme Based on Affine-SIFT (ASIFT) and Structural Similarity (SSIM)

机译:基于仿射SIFT(ASIFT)和结构相似度(SSIM)的面部认证方案

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In this paper, we propose a novel face authentication approach based on affine scale invariant feature transform (ASIFT) and structural similarity (SSIM). The ASIFT descriptor defines key points which are used to match the gallery and probe face images. The matched pairs of key points are filtered based on the location of points in the gallery face image. Then the similarity between sub-images at a preserved pair of matched points is measured by Structural Similarity (SSIM). A mean SSIM (MSSIM) at all pairs of points is computed for authentication. The proposed approach is tested on FERET, CMU-PIE and AR databases with only one image for enrollment. Comparative results on the AR database show that our approach outperforms state-of-the-art approaches.
机译:在本文中,我们提出了一种基于仿射尺度不变特征变换(ASIFT)和结构相似度(SSIM)的新型人脸认证方法。 ASIFT描述符定义了关键点,这些关键点用于匹配图库和探测人脸图像。匹配的关键点对基于图库脸部图像中的点的位置进行过滤。然后,通过结构相似度(SSIM)测量保留的一对匹配点处的子图像之间的相似度。计算所有成对点的平均SSIM(MSSIM)以进行身份​​验证。所提出的方法在FERET,CMU-PIE和AR数据库上进行了测试,仅包含一张要注册的图像。 AR数据库上的比较结果表明,我们的方法优于最新方法。

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