2011 International Conference on Computer Vision 2011
DOI: 10.1109/iccv.2011.6126336
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Fully automatic pose-invariant face recognition via 3D pose normalization

Abstract: An ideal approach to the problem of pose-invariant face recognition would handle continuous pose variations, would not be database specific, and would achieve high accuracy without any manual intervention. Most of the existing approaches fail to match one or more of these goals. In this paper, we present a fully automatic system for pose-invariant face recognition that not only meets these requirements but also outperforms other comparable methods. We propose a 3D pose normalization method that is completely a… Show more

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Cited by 195 publications
(171 citation statements)
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“…These images were captured in four sessions during different periods. Like the previous methods [3,18,17], we evaluate our algorithm on a subset of the MultiPIE database, where each identity has images from all the four sections under seven poses from yaw angles −45…”
Section: Methodsmentioning
confidence: 99%
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“…These images were captured in four sessions during different periods. Like the previous methods [3,18,17], we evaluate our algorithm on a subset of the MultiPIE database, where each identity has images from all the four sections under seven poses from yaw angles −45…”
Section: Methodsmentioning
confidence: 99%
“…2 For the convolutional neural network such as [14], the non-zero values are the same for each row. 3 Note that in the conventional deep model [9], there is a bias term b, so that the output is σ(W x + b). Since W x + b can be written as W x, we drop the bias term b for simplification.…”
Section: Network Architecturementioning
confidence: 99%
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“…However, most existing automated face recognition methods [1][2][3] are devised for the scenario of only frontal face images in the gallery. To recognize non-frontal faces, they usually adopt one of the following three ways: (i) Normalizing the non-frontal probe face to frontal pose and matching it to the enrolled frontal faces [4][5][6], (ii) Generating synthetic face images from the frontal faces in the gallery according to the pose of the probe face, and then comparing the probe face with these synthetic face images [7,8], (iii) Extracting pose-adaptive features directly from the gallery and probe face images for comparison [9,10].…”
Section: Introductionmentioning
confidence: 99%