Face Recognition based Feature Extraction using Principal Component Analysis (PCA)

Novel Feature Extraction for Face Recognition using Multiscale Principal Component Analysis

A method of face recognition based on multiscale principal component analysis (MSPCA) is presented in this paper. Initially face area is extracted from the given face image using Adaboost face detection algorithm. From the face area, regions of interest such as eyes, nose and mouth part are extracted by dividing it along horizontal and vertical directions. Then MSPCA is employed on these region...

Face Recognition Based on Principal Component Analysis

The purpose of the proposed research work is to develop a computer system that can recognize a person by comparing the characteristics of face to those of known individuals. The main focus is on frontal two dimensional images that are taken in a controlled environment i.e. the illumination and the background will be constant. All the other methods of person’s identification and verification lik...

Face Recognition Using Principal Component Analysis

Face recognition is one of the most relevant applications of image analysis. It’s an efficient task (true challenge) to build an automated system with equal human ability to face recognised. Face is a complex 3D visual model and developing a computational model for face recognition is a difficult task. The paper presents a methodology for face recognition based on information theory approach of...

Feature Extraction Using Genetic Algorithm Based Principal Component Analysis

Vision-Based Human Face Recognition Using Extended Principal Component Analysis

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