Ning Xin, Li Weijun, Tang Bo, He Haibo
IEEE Trans Image Process. 2018 Feb 15. doi: 10.1109/TIP.2018.2806229.
This paper develops a new dimensionality reduction method, named Biomimetic Uncorrelated Locality Discriminant Projection (BULDP), for face recognition. It is based on unsupervised discriminant projection and two human bionic characteristics: principle of homology continuity and principle of heterogeneous similarity. With these two human bionic characteristics, we propose a novel adjacency coefficient representation, which does not only capture the category information between different samples, but also reflects the continuity between similar samples and the similarity between different samples. By applying this new adjacency coefficient into the unsupervised discriminant projection, it can be shown that we can transform the original data space into an uncorrelated discriminant subspace. A detailed solution of the proposed BULDP is given based on singular value decomposition. Moreover, we also develop a nonlinear version of our BULDP using kernel functions for nonlinear dimensionality reduction. The performance of the proposed algorithms is evaluated and compared with the state-of-the-art methods on four public benchmarks for face recognition. Experimental results show that the proposed BULDP method and its nonlinear version achieve much competitive recognition performance.
本文提出了一种用于人脸识别的新降维方法，即仿生不相关局部判别投影（BULDP）。该方法基于无监督判别投影以及两种人类仿生特性：同源连续性原理和异质相似性原理。基于这两种人类仿生特性，我们提出了一种新颖的邻接系数表示，它不仅能捕捉不同样本之间的类别信息，还能反映相似样本之间的连续性以及不同样本之间的相似性。通过将这种新的邻接系数应用于无监督判别投影，可以证明我们能够将原始数据空间转换为不相关的判别子空间。基于奇异值分解给出了所提出的BULDP的详细求解方法。此外，我们还使用核函数开发了BULDP的非线性版本用于非线性降维。在所提出的算法在四个人脸识别公共基准上进行了性能评估，并与当前最先进的方法进行了比较。实验结果表明，所提出的BULDP方法及其非线性版本具有很强的竞争识别性能。
