Face Recognition From A Single Training Sample Using Discriminative Multimanifold Technique
Abstract- Face recognition is one of the many challenges in the field of image analysis and computer vision and has many
applications in various domains. The general technique of face recognition usually works better when there are multiple
samples per person (MSSP) is available. In various current applications where face recognition is to be used such as in e-
passport, security systems, identification cards there is only a single sample per person (SSPP) that is readily available. This
less availability of the samples causes failure in the working of conventional face recognition techniques which require
multiple samples per person. To overcome this drawback which sets back the system from the accurate functioning of face
recognition this paper puts forward a novel technique which makes use of discriminative multi-manifold analysis (DMMA)
that extracts distinctive features using image patches. Recognition is done by the process of manifold to manifold matching.
Hence there is an increment in the accuracy rate of face recognition.