Fall 2001 Update to CSU PCA Versus PCA+LDA Comparison
نویسندگان
چکیده
This short paper updates results presented in two previous publications, “ A Nonparametric Statistical Comparison of Principal Component and Linear Discriminant Subspaces for Face Recognition” presented at CVPR 2001 and “Parametric and Nonparametric Methods for the Statistical Evaluation of Human ID Algorithms” presented at the Workshop on the Empirical Evaluation of Computer Vision Algorithms held in conjunction with CVPR 2001. The update reflects changes in the measured performance of our PCA+LDA algorithm following refinements to the numerical precision used to determine the PCA+LDA subspace. The new results show improved performance of the PCA+LDA algorithm relative to PCA algorithm. Where as before, PCA+LDA was clearly inferior to PCA alone, PCA still appears to have a slight edge for this test, but the difference is no longer statistically significant as measured by the methodology laid out in the previous papers. This paper is not intended to be read alone, but instead after the papers cited above. Likewise, those who read the papers above should read this paper to get an improved picture of how the PCA+LDA algorithm performs.
منابع مشابه
The CSU Face Identification Evaluation System: Its Purpose, Features, and Structure
The CSU Face Identification Evaluation System provides standard face recognition algorithms and standard statistical methods for comparing face recognition algorithms. The system includes standardized image pre-processing software, three distinct face recognition algorithms, analysis software to study algorithm performance, and Unix shell scripts to run standard experiments. All code is written...
متن کاملPCA versus LDA
In the context of the appearance-based paradigm for object recognition, it is generally believed that algorithms based on LDA (Linear Discriminant Analysis) are superior to those based on PCA (Principal Components Analysis). In this communication we show that this is not always the case. We present our case rst by using intuitively plausible arguments and then by showing actual results on a fac...
متن کاملThe CSU Face Identification Evaluation System User’s Guide: Version 5.0
The CSU Face Identification Evaluation System provides standard face recognition algorithms and standard statistical methods for comparing face recognition algorithms. This document describes Version 5.0 the Colorado State University (CSU) Face Identification Evaluation System. The system includes standardized image pre-processing software, four distinct face recognition algorithms, analysis so...
متن کاملThe CSU Face Identification Evaluation System User’s Guide: Version 4.0
The CSU Face Identification Evaluation System provides standard face recognition algorithms and standard statistical methods for comparing face recognition algorithms. This document describes Version 4.0 the Colorado State University (CSU) Face Identification Evaluation System. The system includes standardized image pre-processing software, three distinct face recognition algorithms, analysis s...
متن کاملLine-Based PCA and LDA Approaches for Face Recognition
Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) techniques are important and well-developed area of image recognition and to date many linear discrimination methods have been put forward. Despite these efforts, there persist in the traditional PCA and LDA some weaknesses. In this paper, we propose a new Line-based methodes called Line-based PCA and Line-based LDA that ...
متن کامل