Comparative Study of Face Recognition Algorithms
نویسنده
چکیده
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 performance of these algorithms was compared for various combinations of training data and testing data. The observations and results are presented in this report.
منابع مشابه
A Comparative Study of Gender and Age Classification in Speech Signals
Accurate gender classification is useful in speech and speaker recognition as well as speech emotion classification, because a better performance has been reported when separate acoustic models are employed for males and females. Gender classification is also apparent in face recognition, video summarization, human-robot interaction, etc. Although gender classification is rather mature in a...
متن کاملIllumination compensation and normalization in eigenspace-based face recognition: A comparative study of different pre-processing approaches
The aim of this work is to investigate illumination compensation and normalization in eigenspace-based face recognition by carrying out an independent comparative study among several pre-processing algorithms. This research is motivated by the lack of direct and detailed comparisons of those algorithms in equal working conditions. The results of this comparative study intend to be a guide for t...
متن کاملFace Recognition in Thermal Images based on Sparse Classifier
Despite recent advances in face recognition systems, they suffer from serious problems because of the extensive types of changes in human face (changes like light, glasses, head tilt, different emotional modes). Each one of these factors can significantly reduce the face recognition accuracy. Several methods have been proposed by researchers to overcome these problems. Nonetheless, in recent ye...
متن کاملFace Detection at the Low Light Environments
Today, with the advancement of technology, the use of tools for extracting information from video are much wider in terms of both visual power and the processing power. High-speed car, perfect detection accuracy, business diversity in the fields of medical, home appliances, smart cars, humanoid robots, military systems and the commercialization makes these systems cost effective. Among the most...
متن کاملAnalysis of Recognition Accuracy Using Curvelet Tranform
This paper describes a comparative analysis of recognition accuracy using feature extraction algorithm. A feature extraction algorithm is introduced for face recognition, Principle Component Analysis (PCA),Linear Discriminant Analysis(LDA) , Independent Component Analysis(ICA) and Nonnegative matrix factorization (NMF) based on curvelet transform. Mostly recognition system is capable to perform...
متن کاملFace Recognition Based Rank Reduction SVD Approach
Standard face recognition algorithms that use standard feature extraction techniques always suffer from image performance degradation. Recently, singular value decomposition and low-rank matrix are applied in many applications,including pattern recognition and feature extraction. The main objective of this research is to design an efficient face recognition approach by combining many tech...
متن کامل