基于PCA和几何方法的人脸识别

N. Tummala, P. C. Sekhar
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引用次数: 9

摘要

本文提出了一个人脸识别问题的框架。近年来,人脸识别受到了研究人员的广泛关注,但由于噪声的存在,人脸识别在实时应用中仍然面临诸多难题。在过去的几年中,已经提出了广泛的人脸识别技术,这些技术主要属于基于特征或基于整体的方法。在本文中,我们使用了一些基于几何的方法,即映射人脸的不同基点并对它们进行比较,以有效地识别人脸和相应的数据检索。将主成分分析算法与几何方法相融合用于人脸识别。本文通过不同的实验展示了我们的方法的力量,并生动地集中在最佳的相似度和接近度以及最高的识别率上。
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 using PCA and geometric approach
This paper presents a framework for the face recognition problem. In recent times face recognition had been paid ample attention from researchers but still remained confronting in real time applications due to the presence of noise. A wide range of face recognition techniques have been presented in the past few years which majorly fall under feature based or holistic based approaches. In our paper we use some ailments of geometric based approach which maps different fiducial points in the face and compares them, for effective recognition of faces and respective data retrieval. We use the principal component analysis algorithm in fusion with geometric approach for face recognition purpose. This paper demonstrates the power of our approach by using different experiments and vividly concentrates on the best similarity and proximity possible coupled with highest recognition rate.
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