Abstract
SVD and FFT are both the efficient tools for image analysis and face recognition. In this paper, we first study the role of SVD and FFT in both filed. Then the decomposition information from SVD and FFT are compared. Next, a new viewpoint that the singular value matrix contains the illumination information of the image is proposed and testified by the experiments based on the ORL face database finally.
Highlights
As the most natural and important characteristic, face plays the central role in various forms of human biological characteristics
We first study the role of singular value decomposition (SVD) and Fast Fourier Transform (FFT) in both filed
Face recognition technology has gone through three stages of change: The first stage (1964-1990) is usually only as a general pattern recognition problem to study; In the second stage (1991-1997), face recognition technology gained rapid development, the proposed algorithm greatly improved the efficiency and accuracy; In the third stage (1998-), face recognition technology gradually tend to mature, research focuses on solving the problems of poor robustness
Summary
As the most natural and important characteristic, face plays the central role in various forms of human biological characteristics. The fifth step is training and this process may be known as the classifier design. The end of this process will generate the parameters can be used to identify. In this part, the basic approach is to determine a decision rule based on the sample training set, so that when we classify objects, error recognition rate or error is the smallest. Recognition of face images acquired in an outdoor environment with changes in illumination and or pose remains a largely unsolved problem.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
