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 face recognition 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.