Encoding Local Binary Patterns using the re-parametrization of the second order Gaussian jet
Encoding Local Binary Patterns using the re-parametrization of the second order Gaussian jet
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DOI：
10.1109/fg.2013.6553709
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发表时间：
2013-04
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影响因子：
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通讯作者：
John A. Ruiz-Hernandez;M. Pietikäinen
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文献类型：
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作者：
John A. Ruiz-Hernandez;M. Pietikäinen
在目标识别中，在几乎所有文献中提出的方法中，稳健的特征集被认为是一个重要的组成部分。在人脸分析中，最著名的特征集之一是基于局部二值模式(LBP)的，它通过区域内像素之间的比较来提取图像中包含的信息，最后将这种比较以直方图的形式进行编码。我们认为这种编码在统计上是不稳定的，并且会在识别过程中导致错误，特别是在噪声和低分辨率图像中，图像中包含的信息不足以生成统计上稳健的直方图。在本文中，我们提出了一种新的局部二值模式编码方法，通过对二次局部阶高斯喷注的重新参数化，可以生成更稳健和可靠的直方图，适用于不同的人脸分析任务。实验结果表明，该方法可用于在自发性微表情语料库(SMIC)和YORK欺骗检测测试中识别具有竞争性的微表情。
In object recognition a robust feature set is considered as an important component in almost all the approaches proposed in the literature. In facial analysis, one of the best known feature set is based in Local Binary Patterns (LBP) which extracts the information contained in the image using comparisons between pixels in a region, finally such comparisons are encoded in form of histogram. We argue that this kind of encoding is statistically non-stable and can lead to errors during the recognition process, specially in noisy and low-resolution images, where the information contained in the image is not enough to generate a statistically robust histogram. In this paper, we propose a new method to encode the Local Binary Patterns using an re-parametrization of the second local order Gaussian Jet which generates more robust and reliable histograms suitable for different facial analysis tasks. We show that our method can be used for recognizing micro-expressions with competitive performances on the Spontaneous Micro-expression Corpus (SMIC) and the YORK Deception Detection Test.