人脸识别:基于模板的方法

T. Archana, T. Venugopal
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引用次数: 8

摘要

本文提出了一种基于模板的人脸识别方法。在这里,我们将我们的方法与基于整体特征的主成分分析(PCA)方法进行了比较。PCA是一种基于统计特征的特征空间分析方法。PCA是一种简单的仅对正面人脸进行识别的方法,该系统基于灰度模板匹配。为了了解所提出的系统的伟大,我们进行了实验,并与现有系统进行了系统的比较,以检查系统的性能。我们观察到,PCA的识别率正确率或效率仅为70-75%左右,当存在光照、姿态、平面内旋转、噪声等变化时,PCA无法识别人脸。在查询中输入图像。在模板匹配方面,我们观察到模板匹配识别的结果优于PCA(大于等于20%),模板匹配识别过程可以有效地识别人脸,并且对上述所有因素都具有不变性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Face recognition: A template based approach
In this paper, we proposed a template based face recognition approach. Here we compared our approach with the holistic feature based approach Principal Component Analysis (PCA). PCA is a statistical feature based approach works on Eigen space. PCA is a simple approach for face recognition of only frontal faces and proposed system is based on grey level template matching. To know the greatness of proposed system we have done experiment and compared with existing systems in a systematic way to check the performance of the systems. We observed that the correctness or efficiency of recognition rate using PCA is only about 70-75%, PCA was not able to recognize the faces if there is change in illumination, pose, in-plane rotation, noise etc.,. in the query input image. Where as for template matching we observed that got better results (more than or equal to 20%) than PCA, template matching recognition process can recognize the faces efficiently and invariant to all above factors.
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