We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Virtual samples and sparse representation-based classification algorithm for face recognition.
- Authors
Yali Peng; Lingjun Li; Shigang Liu; Jun Li; Han Cao
- Abstract
Due to the environment and equipment are not controllable, the process of face image acquisition is inevitable to be interfered by external factors, and there are usually only a small number of available face images. Insufficient samples are not conducive to face recognition. Therefore, it is a popular scheme to produce virtual samples based on the available training samples. In this study, the authors first take the symmetry of human face into account, and propose a novel method to generate virtual samples. Then a representation-based classification method and the score fusion strategy are applied to both original face images and virtual images to perform face recognition. Several sparse representation-based classification algorithms are compared on ORL, FERET and GT databases. Experimental results show that the authors' method is effective for improving the face recognition.
- Subjects
HUMAN facial recognition software; IMAGE processing; CLASSIFICATION algorithms; IMAGE fusion; IMAGE databases
- Publication
IET Computer Vision (Wiley-Blackwell), 2019, Vol 13, Issue 2, p172
- ISSN
1751-9632
- Publication type
Article
- DOI
10.1049/iet-cvi.2018.5096