In this paper we address the problem of face
detection and recognition of grey scale frontal view images. We
propose a face recognition system based on probabilistic neural
networks (PNN) architecture. The system is implemented using
voronoi/ delaunay tessellations and template matching. Images
are segmented successfully into homogeneous regions by virtue
of voronoi diagram properties. Face verification is achieved
using matching scores computed by correlating edge gradients
of reference images. The advantage of classification using PNN
models is its short training time. The correlation based
template matching guarantees good classification results
Description:
Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on
n this paper we address the problem of face
detection and recognition of grey scale frontal view images. We
propose a face recognition system based on probabilistic neural
networks (PNN) architecture. The system is implemented using
voronoi/ delaunay tessellations and template matching. Images
are segmented successfully into homogeneous regions by virtue
of voronoi diagram properties. Face verification is achieved
using matching scores computed by correlating edge gradients
of reference images. The advantage of classification using PNN
models is its short training time. The correlation based
template matching guarantees good classification results.