A novel framework for face recognition using ASM and sparse coding
Arulmurugan Ramu, Laxmi Priya M.R Arulmurugan R · 2013
Face recognition system is an application that is used to identifying or verifying a person from a digital image, which can be done by comparing selected facial features from the image and a facial database, but robust commercial applications are still lacking. Face images used present variations in pose, illumination, image quality, and resolution. The benefits of using image quality and reliability is to improve the accuracy. Face recognition (principal component analysis (PCA) to assess the feasibility of real world face recognition, but the system performance are low when the image in uncontrolled poses. Active shape models (ASMs) are statistical model, which iteratively deform to fit to a new image. The shapes are constrained by the PDM (Point Distribution Model) which is statistical shape model, to vary only in training set of labelled examples. Then weighted matching will be applied between the input image and database images. This method provide the better recognition performance when compare to the Existing methods. Here we detect the face by the Active shape model Algorithm. It is reliable to uncontrolled pose images.