Research of Face Recognition Based on Locally Linear Embedding

Cuihong Zhou, Gelan Yang
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Abstract

Image data taken with various capturing devices are usually multidimensional and therefore they are not very suitable for accurate classification normally expecting to operate only on a small set of relevant features. Locally Linear Embedding is an effective nonlinear dimensionality reduction method for exploring the intrinsic characteristics of high dimensional data. In this paper, novel Local linear embedding for face classification is proposed. We modify the LLE algorithm by preserving more geometrical knowledge of the high-dimensional data, then combining with simple classifiers such as the nearest mean classifier. Experimental simulations are shown to yield remarkably good classification results in high dimension face image sequence.
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基于局部线性嵌入的人脸识别研究
使用各种捕获设备拍摄的图像数据通常是多维的,因此它们不太适合用于准确分类,通常期望仅对一小部分相关特征进行操作。局部线性嵌入是探索高维数据内在特征的一种有效的非线性降维方法。本文提出了一种新的局部线性嵌入人脸分类方法。我们通过保留更多高维数据的几何知识来改进LLE算法,然后结合最接近均值分类器等简单分类器。实验仿真结果表明,该方法对高维人脸图像序列具有很好的分类效果。
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