This paper describes SCAN descriptor as a local face descriptor to represent a face image. SCAN techniques that originally for image compression and data hiding were used to locally extract face image features to represent the face image. Simulations were conducted on the subset of cropped Yale Face Database B by either varying uniformly the face image pixels (intensities) or lowering their resolutions in the database subset. The simulation results show that SCAN descriptor has recognition rate that outperforms for both either two global face descriptors, i.e. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), or two local face descriptors, i.e. Local Binary Pattern (LBP) and Multi-Scale Local Binary Pattern (MLBP).
|Number of pages||4|
|Journal||ARPN Journal of Engineering and Applied Sciences|
|Publication status||Published - 1 Jan 2014|
- Global face descriptor
- Local face descriptor
- Recognition rate