Face Recognition Using Holistic Based Approach
Vandana S. Bhat, Jagadeesh Pujari · 2014
Face recognition is the highest generous method for identification of an individual. Principal Component Analysis (PCA) is an efficient technique to identify a face from a given image. For a static image holistic based approach uses the entire raw face image as input and feature based method is based on extracting local facial features, and geometric, appearance properties. To recognize a face two image processing steps are available. In the first step face detection process is carried out using Viola Jones face detector. In the second phase it describes how to build a simple, yet a complete face recognition system using Principal Component Analysis, a Holistic approach. Linear projection is applied to the original image space to achieve dimensionality reduction and the functionality in executed out by projecting face images onto a feature space that spans the significant variations among known face images. Next step is to project the extracted face image on to a set of face space that represents significant variations among the known face images. Face will be categorized as known or unknown face after matching with the stored database. Evaluation performance of various parameters such as distance classifier used, applying histogram equalization and selecting the number of eigenfaces, we propose a system which combines these above mentioned features into one face recognition system. Thus it helps a learning mechanism to recognize new faces in an unsupervised manner.