一种改进的LBP分块人脸识别方法

N. Kumar, Sunny Behal
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引用次数: 2

摘要

人脸识别被认为是数字图像处理中最棘手、最关键的前沿领域之一。人类的大脑也使用类似的技术来进行面部识别。当仔细观察一张脸时,人类的大脑会表示结果。除了自动处理系统外,由于图像在面积、大小、清晰度和姿态等方面的变化,这种技术非常复杂。在本文中,作者使用了原生二值模式和统一原生二值模式的选项来进行人脸识别。他们在公开可用的基准ORL图像数据库上计算了许多分类器来验证所提出的方法。结果清楚地表明,所提出的LBP块精明策略优于传统的LBP系统。
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An Improved LBP Blockwise Method for Face Recognition
Face recognition is considered as one of toughest and most crucial leading domains of digital image processing. The human brain also uses a similar kind of technique for face recognition. When scrutinizing a face, the human brain signifies the result. Aside from AN automatic processing system, this technique is very sophisticated, owing to the image variations on account of the picture varieties in as far as area, size, articulation, and stance. In this article, the authors have used the options of native binary pattern and uniform native binary pattern for face recognition. They compute a number of classifiers on publicly available benchmarked ORL image databases to validate the proposed approach. The results clearly show that the proposed LBP-piece shrewd strategy has outperformed the traditional LBP system.
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