A key to dictionary learning is to attain a robust dictionary, which enables difference between test samples and training samples of the same class to be alleviated. Owing to this factor, the dictionary can bring proper representations of test samples and produce better classification results for them. For face recognition, because of varying facial appearance caused by changeable illuminations, poses and facial expressions, a robust dictionary is definitely preferred. In this paper, we propose a robust dictionary learning method for face recognition. Robustness is attained in a two-fold way. First, auxiliary faces are produced via original face images. Second, the scheme to attain the dictionary under the condition that label coefficients can deviate from sample coefficients is designed. Auxiliary faces express possible variations of faces. Moreover, it seems that difference between auxiliary faces and original training samples of the same class somewhat reflects difference between test samples and training samples, thus use of auxiliary faces is beneficial to improve robustness of the method. The scheme to attain the dictionary further enhances robustness.
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.