Abstract:
In this paper, a contemporary method for face
recognition i.e. local gaussian difference extrema pattern
(LGDEP) is proposed. The proposed method collects the most
prominent directional edges present in the image, which in turn
extracts the most prominent edge information. The basic local
binary pattern (LBP) encodes the central pixel with reference to
its surrounding pixels in an image while the directional local
extrema pattern (DLEP) encodes the directional edge
information featured by local extrema in 0°,45°,90° and 135° in
an image. Whereas the proposed method encodes the most
prominent directional edge information by integrating the local
extrema concept with the multi-resolution (three resolutions)
Gaussian filter banks. The Gaussian filter banks make the
method more robust as it extracts only the most prominent edges
and ignores (smoothens) the minor variations due to noise.
Finally, k-nearest neighbors (KNN) classifier is used for the
classification. The performance of the proposed method is
evaluated on three different sets of FERET Database and
compared with the standard block based LBP (BLBP). After
thorough analysis, it has been found that the proposed method
significantly improves the results from 95% (BLBP) to 100% in
terms of recognition rate.
