Face recognition has broad applications, and it is a difficult problem since face image can change with photographic
conditions, such as different illumination conditions, pose changes and camera angles. How to obtain some invariable
features for a face image is the key issue for a face recognition algorithm. In this paper, a novel tensor structure of face
image is proposed to represent image features with eight directions for a pixel value. The invariable feature of the face
image is then obtained from gradient decomposition to make up the tensor structure. Then the singular value
decomposition (SVD) and principal component analysis (PCA) of this tensor structure are used for face recognition.
The experimental results from this study show that many difficultly recognized samples can correctly be recognized,
and the recognition rate is increased by 9%-11% in comparison with same type of algorithms.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.