Face image recognition using PCA, 2DCT and neural network

Mirabeau, Nguela

dc.contributor.advisor	Wang, Zenghui
dc.contributor.author	Mirabeau, Nguela
dc.date.accessioned	2020-03-12T13:50:45Z
dc.date.available	2020-03-12T13:50:45Z
dc.date.issued	2018-07
dc.identifier.uri	http://hdl.handle.net/10500/26330
dc.description	Text in English	en
dc.description.abstract	Automatic face recognition is a very important research area in computer science since it has been widely used in security systems. During the last two decade, the research area of face recognition has focused much attention from the scientific communities with the aim to provide highly intelligent human-machine interaction with high performance. This study proposes a system that encompasses a reduction of significant variable features using the principal components analysis on one hand, feature extraction using 2DCT on the other hand and then a combined method using both. Afterward each sample image is classified according to their pattern class using a Neural Network. The experimental results obtained shows an improvement in term of recognition rate when we combined the two methods.	en
dc.format.extent	1 electronic resource (vi, 86 leaves) : color illustration
dc.language.iso	en	en
dc.subject	Principal component analysis	en
dc.subject	Neural network	en
dc.subject	Two-dimensional cosine transforms	en
dc.subject	Eigenvalues and eigenvectors	en
dc.subject	Covariance matrix	en
dc.subject	Feed forward network	en
dc.subject	Neural network	en
dc.subject.ddc	006.2483995
dc.subject.lcsh	Human face recognition (Computer science)
dc.subject.lcsh	Neural networks (Computer science)
dc.title	Face image recognition using PCA, 2DCT and neural network	en
dc.type	Dissertation	en
dc.description.department	Electrical and Mining Engineering	en
dc.description.degree	M.Tech (Electrical Engineering)