基于模糊度量和监督距离保持投影的模糊人脸识别

Mohiuddin Muhi, S. Jahan, Md. Anwarul Islam Bhuiyan
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引用次数: 0

摘要

本文主要研究非受控场景下的人脸识别技术,特别是模糊人脸图像的识别。首先,使用最近提出的模糊度量来确定测试图像的模糊程度。该模糊度量值用于使用高斯滤波器模糊图库图像的训练集。训练图像的模糊程度与测试图像相同。有监督距离保持投影(SDPP)的两种变体,即半定最小二乘(SLS-SDPP)和正则化有监督距离保持投影(RSDPP),用于提取训练和测试图像的有效特征。使用k近邻分类器进行匹配。在ORL和Yale两个基准人脸数据上进行了数值实验。将SLS-SDPP和RSDPP的性能与其中一种主流方法特征面法进行了比较。实验结果表明,模糊度量和特征提取相结合的方法在识别不同层次的模糊图像方面取得了优异的效果,并且优于基本方法和特征面方法。达卡大学学报,69(3):154-160,2022 (6)
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Blur Face Recognition using Blur Metric and Some Variants of Supervised Distance Preserving Projection
This paper is focused on face recognition techniques in uncontrolled scenarios, specifically on the recognition of face images with blur effects. At first, the blur level of the testing image is determined using recently proposed blur metric. This blur metric value is used to blur the training set of gallery images using Gaussian filter. The blur level of training images is the same as that of the testing image. Two variants of Supervised Distance Preserving Projection (SDPP), SDPP as Semidefinite Least Square (SLS-SDPP) and Regularized Supervised Distance Preserving Projection (RSDPP), are used for extracting effective features of training and testing images. K-Nearest Neighbor classifier is used for matching. Numerical experiments were carried out on two benchmarking face data ORL and Yale. The performances of SLS-SDPP and RSDPP are compared with one of the leading methods Eigenface method. Experimental results show that the combination of blur metric and the feature extraction methods achieved outstanding performance in recognizing blur images of different levels and also outperforms the base methods and Eigenface method. Dhaka Univ. J. Sci. 69(3): 154-160, 2022 (June)
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