Block-wise 2D kernel PCA/LDA for face recognition-笔记

最新推荐文章于 2024-09-19 22:04:40 发布

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原文链接：http://www.cnblogs.com/xiaolong19910914/p/8548180.html

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#人工智能

本文提出了一种基于核技术的2D特征提取算法框架，专门用于人脸识别，该框架通过引入块状模型和更高阶的统计依赖性，显著提升了人脸识别的准确性和可靠性。同时，文中也讨论了2D-KPCA面临的计算复杂性和局部特性学习不足的问题。

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In the present work, we propose a framework for kernel-based 2D feature extraction algorithms tailored to face recognition .
extending 2D-PCA/LDA in the following two aspects:
(1)kernel technique is incorporated to capture the higher order statistical dependencies among the rows of input images. Thus, face recognition results are considerably improved
(2)A block-wise model for face recognition is introduced and proved to be reliable through our experiments.

2D-KPCA所带来的问题：

（1）Computational complexity: unlike direct extension of 2D-PCA/LDA to kernel-induced feature space which is computationally intractable, proposed B2D-KPCA/GDA perform subspace projection inside each block manifold, significantly alleviating computational cost
（2）Locality: as widely known, distribution of face images is both multimodal and highly nonlinear [7,17]. Direct extension of 2D-PCA/LDA to kernel-induced feature space, overcomes the shortcomings of 2D-PCA/LDA in extracting global nonlinear structure of input data, but fails to learn local characteristics of input data.

转载于:https://www.cnblogs.com/xiaolong19910914/p/8548180.html