Abstract
At present there are many methods that could deal well with frontal view face recognition when there is sufficient number of representative training samples. However, few of them can work well when only one training sample per class is available. In this paper, we present a method of face recognition based on wavelet low-frequency band and singular value decomposition (SVD) to solve the one training sample problem. To acquire more information from the single training sample, training image is linearly combined with its reconstructed image of wavelet low-frequency band into a new training image. By using Fourier transform, the spectrum representation of face image is obtained that is invariant against spatial translation. Then the spectrum representation is projected into a uniform eigen-space that is obtained from SVD of standard face image and the coefficient matrix is used as feature for recognition. The proposed algorithm obtains acceptable experimental results on the ORL face database.
Sign-in/Register to access full text options 
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 callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
