Real Time Face Detection and Recognition using Haar - Based Cascade Classifier and Principal Component Analysis

Face is a complex multidimensional visual model and developing a computational model for face recognition is difficult. The paper presents a methodology for facerecognition based on information theory approach of coding and decoding the face image. Proposed methodology is connection of two stages – Face detection using Haar Based Cascade classifier and recognition using Principle Component analysis. Study of the paper include the system to find the locations of Log-Gabor features with maximal magnitudes at single scale and multiple orientations using sliding window based search and then use the same feature locations for all other scales. The goal is to implement the system (model) for a particular face and distinguish it from a large number of stored faces with some real-time variations as well.