原始与处理:如何使用原始和处理图像鲁棒人脸识别在不同的照明

Li Xu, Lei Huang, Chang-ping Liu
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引用次数: 2

摘要

以往的许多图像处理方法都是丢弃图像的低频分量,提取光照不变量来进行人脸识别。然而,这种方法可能会导致处理后的图像失真,并且在正常照明下表现不佳。本文提出了一种处理人脸识别中光照问题的新方法。首先,我们定义一个分数来表示查询输入与图库类中的个体之间的第一大和第二大相似性的相对差异。然后,根据分数,我们选择合适的图像,无论是原始图像还是处理过的图像,进行识别。在ORL、CMU-PIE和Extended Yale B人脸数据库中进行的实验表明,该方法组合后具有更强的鲁棒性,并且优于传统的融合算子、相似度和和最大值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Raw vs. Processed: How to Use the Raw and Processed Images for Robust Face Recognition under Varying Illumination
Many previous image processing methods discard low-frequency components of images to extract illumination invariant for face recognition. However, this method may cause distortion of processed images and perform poorly under normal lighting. In this paper, a new method is proposed to deal with illumination problem in face recognition. Firstly, we define a score to denote a relative difference of the first and second largest similarities between the query input and the individuals in the gallery classes. Then, according to the score, we choose the appropriate images, raw or processed images, to involve the recognition. The experiment in ORL, CMU-PIE and Extended Yale B face databases shows that our adaptive method give more robust result after combination and perform better than the traditional fusion operators, the sum and the maximum of similarities.
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