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首页> 外文期刊>Pattern Analysis and Machine Intelligence, IEEE Transactions on >Facial Deblur Inference Using Subspace Analysis for Recognition of Blurred Faces
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Facial Deblur Inference Using Subspace Analysis for Recognition of Blurred Faces

机译：使用子空间分析的人脸去模糊推理用于模糊人脸识别

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摘要

This paper proposes a novel method for recognizing faces degraded by blur using deblurring of facial images. The main issue is how to infer a Point Spread Function (PSF) representing the process of blur on faces. Inferring a PSF from a single facial image is an ill-posed problem. Our method uses learned prior information derived from a training set of blurred faces to make the problem more tractable. We construct a feature space such that blurred faces degraded by the same PSF are similar to one another. We learn statistical models that represent prior knowledge of predefined PSF sets in this feature space. A query image of unknown blur is compared with each model and the closest one is selected for PSF inference. The query image is deblurred using the PSF corresponding to that model and is thus ready for recognition. Experiments on a large face database (FERET) artificially degraded by focus or motion blur show that our method substantially improves the recognition performance compared to existing methods. We also demonstrate improved performance on real blurred images on the FRGC 1.0 face database. Furthermore, we show and explain how combining the proposed facial deblur inference with the local phase quantization (LPQ) method can further enhance the performance.
机译：本文提出了一种通过对人脸图像进行去模糊来识别由于模糊而退化的人脸的新方法。主要问题是如何推断代表脸部模糊过程的点扩散函数（PSF）。从单个面部图像推断PSF是一个不适的问题。我们的方法使用从模糊面部训练集中获得的先验信息，使问题更易于处理。我们构造一个特征空间，以使由相同PSF降级的模糊面孔彼此相似。我们学习统计模型，这些统计模型表示此功能空间中预定义PSF集的先验知识。将未知模糊的查询图像与每种模型进行比较，并选择最接近的一种进行PSF推理。使用与该模型相对应的PSF对查询图像进行去模糊处理，从而可以对其进行识别。在因聚焦或运动模糊而人为降级的大型人脸数据库（FERET）上进行的实验表明，与现有方法相比，我们的方法大大提高了识别性能。我们还在FRGC 1.0面部数据库上展示了对真实模糊图像的改进性能。此外，我们展示并说明了如何将拟议的面部去模糊推理与局部相位量化（LPQ）方法相结合可以进一步提高性能。

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