图 书 馆
基于图像集的人脸识别Face Recognition Based on Image Sets |
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| 课程网址: | http://videolectures.net/cvpr2010_cevikalp_frbi/ |
| 主讲教师: | Hakan Cevikalp |
| 开课单位: | 埃斯基谢尔奥斯曼齐大学 |
| 开课时间: | 2010-07-19 |
| 课程语种: | 英语 |
| 中文简介: |  我们介绍了一种新的图像集人脸识别方法。在我们的设置中，每个测试和训练示例都是一组个人脸部的图像，而不仅仅是单个图像，因此识别决策需要基于图像集的比较。用于此的方法具有两个主要方面：用于表示各个图像集的模型;以及用于比较模型的相似性度量。在这里，我们将图像表示为线性或仿射特征空间中的点，并通过由其特征点跨越的凸几何区域（仿射或凸包）来表征每个图像集。通过凸模型之间的几何距离（最接近的距离）来测量设置相异性。为了减少异常值的影响，我们使用稳健的方法来丢弃远离拟合模型的输入点。核心技巧允许将方法扩展到隐式特征映射，从而处理面部图像的复杂和非线性流形。在两个公共人脸数据集上的实验表明，我们提出的方法优于现有的许多方法。 |
| 课程简介: | We introduce a novel method for face recognition from image sets. In our setting each test and training example is a set of images of an individual’s face, not just a single image, so recognition decisions need to be based on comparisons of image sets. Methods for this have two main aspects: the models used to represent the individual image sets; and the similarity metric used to compare the models. Here, we represent images as points in a linear or affine feature space and characterize each image set by a convex geometric region (the affine or convex hull) spanned by its feature points. Set dissimilarity is measured by geometric distances (distances of closest approach) between convex models. To reduce the influence of outliers we use robust methods to discard input points that are far from the fitted model. The kernel trick allows the approach to be extended to implicit feature mappings, thus handling complex and nonlinear manifolds of face images. Experiments on two public face datasets show that our proposed methods outperform a number of existing state-of-the-art ones. |
| 关 键 词: | 人脸识别; 图像集; 隐式特征映射 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-03-12：lxf |
| 阅读次数: | 85 |