Comparative Study of Diverse Face Recognition Approaches along with Intrinsic Worth and Recognition Rate

Comparative Study between Different Eigenspace-Based Approaches for Face Recognition

Different eigenspace-based approaches have been proposed for the recognition of faces. They differ mostly in the kind of projection method been used and in the similarity matching criterion employed. The aim of this paper is to present a comparative study between some of these different approaches. This study considers theoretical aspects as well as simulations performed using a face database w...

Comparative Study of Face Recognition Algorithms

This project reviews some of the pattern classification algorithms used for face recognition viz., Principal Component Analysis, Linear Discriminant Analysis, Linear Discriminant Analysis of Principal components, Multiple Exemplar Discriminant Analysis and Bayesian Maximum Likelihood based similarity matching technique. These algorithms were implemented and tested on a ’POSE’ database. The perf...

Intrinsic Divergence for Face Recognition

Comparative Analysis of Face Recognition Approaches: A Survey

In recent days, the need of biometric security system is heightened for providing safety and security against terrorist attacks, robbery, etc. The demand of biometric system has risen due to its strength, efficiency and easy availability. One of the most effective, highly authenticated and easily adaptable biometric security systems is facial feature recognition. This paper has covered almost a...

A New Fast and Efficient HMM-Based Face Recognition System Using a 7-State HMM Along With SVD Coefficients

In this paper, a new Hidden Markov Model (HMM)-based face recognition system is proposed. As a novel point despite of five-state HMM used in pervious researches, we used 7-state HMM to cover more details. Indeed we add two new face regions, eyebrows and chin, to the model. As another novel point, we used a small number of quantized Singular Values Decomposition (SVD) coefficients as feature...