Performance Evaluation of Face Recognition using PCA and N-PCA
Performance Evaluation of Face Recognition using PCA and N-PCA
复制标题
使用 PCA 和 N-PCA 进行人脸识别的性能评估
DOI：
10.5120/13266-0753
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Pankaj Chawla
中科院分区：
文献类型：
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作者：
A. Bansal;Pankaj Chawla
人脸识别已经成为刑事调查人员使用的一种有价值的常规法医工具。它是计算机视觉研究的一个重要领域，近年来引起了人们的极大兴趣。提高安全性的努力，如自动监控和在身份识别中使用生物识别技术，是这种兴趣增加的部分原因。然而，在提高光照变化、姿态变化、遮挡和图像分辨率下的人脸识别的准确性方面仍然存在一些挑战。本文提出了性能比较的人脸识别主成分分析（PCA）和归一化主成分分析（N-PCA）。实验进行了ORL，印度人脸数据库和格鲁吉亚技术人脸数据库，其中包含在表情，姿势和面部细节的变化。通过改变训练图像的数量来比较两种方法获得的结果，并且已经发现，随着训练图像的数量增加，效率也增加。实验结果还表明，N-PCA比PCA具有更好的效果。一般条款
Face recognition has become a valuable and routine forensic tool used by criminal investigators. It is an important area of computer vision research and has gained significant interest in recent years. Efforts in improving security, such as automatic surveillance and the use of biometrics in identification, are partly responsible for this increased interest. However, several challenges remain in improving the accuracy of face recognition under illumination changes, variations in pose, occlusions, and image resolution. This paper presents performance comparison of face recognition using Principal Component Analysis (PCA) and Normalized Principal Component Analysis (N-PCA). The experiments are carried out on the ORL, Indian face database and Georgia Tech face database which contain variability in expression, pose, and facial details. The results obtained for the two methods have been compared by varying the number of training images and it has been found that as the number of training images increases efficiency also increases. The result also shows that N-PCA gives better results than PCA. General Terms