IEEE Project Titles, FACE RECOGNITION USING LAPLACIAN FACES

.Net Projects ( major )	Java Projects ( major )	VB Projects ( major )	Php Projects ( major )	Vc++ Projects ( major )	SQL projects ( major )	IEEE ECE Projects
C&Ds projects ( major )	C++ projects ( major )	Perl projects ( major )	Cold Fusion Projects	ASP projects ( major )	Delphi projects ( major )	IEEE CSE Projects

Custom Search

What is 1000Projects
'1000projects.com' is an educational content website dedicated to finding and realizing Final Year Projects, IEEE Projects, Engineering Projects, Science Fair Projects, Project Topics, Project Ideas, Major Projects, Mini Projects, Paper Presentations, Presentation Topics, IEEE Topics, .Net Projects, Java Projects, PHP Projects, VB Projects, SQL Projects, C & DS Projects, C++ Projects, Perl Projects, ASP Projects, Delphi Projects, HTML Projects, Cold Fusion Projects, Java Script Projects, Btech Projects, BE Projects, MCA Projects, Mtech Projects, MBA Projects, Project on Software, CBSE Projects, Testing Projects, Embedded Projects, Chemistry Projects, Electronics Projects, Electrical Projects, Science Projects, Mechanical Projects, Mba project Reports, Placement papers, Sample Resumes, Entrance Exams, Technical Faq's, Puzzles, etc

how it works?

Everything on this site is submitted by the students in this professional community. You Can submit your Projects, Project Topics & Ideas to info.1000projects{at}gmail.com after you submit your project/project Idea/Abstract/Seminar Topics, These are being verified and approved by our administrator. after approval of this project/project Idea/Abstract/Seminar Topics, It can be shown on 1000projects.com so that other users can read/discuss it.The entire content on this website is Only For Educational Purpose, Non Commercial use!

Please help us/Other Users by sending projects/project Ideas/Abstracts/Seminar Topics. Thanking You!!!!!

FACE RECOGNITION USING LAPLACIAN FACES
PATTERN ANALYSIS AND MACHINE INTELLIGENCE
JAVA
Abstract: The face recognition is a fairly controversial subject right now. A system such as this can recognize and track dangerous criminals and terrorists in a crowd, but some contend that it is an extreme invasion of privacy. The proponents of large-scale face recognition feel that it is a necessary evil to make our country safer. It could benefit the visually impaired and allow them to interact more easily with the environment. Also, a computer vision-based authentication system could be put in place to allow computer access or access to a specific room using face recognition. Another possible application would be to integrate this technology into an artificial intelligence system for more realistic interaction with humans. We propose an appearance-based face recognition method called the Laplacianface approach. By using Locality Preserving Projections (LPP), the face images are mapped into a face subspace for analysis. Different from Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) which effectively see only the Euclidean structure of face space, LPP finds an embedding that preserves local information, and obtains a face subspace that best detects the essential face manifold structure. The Laplacian faces are the optimal linear approximations to the eigen functions of the Laplace Beltrami operator on the face manifold. In this way, the unwanted variations resulting from changes in lighting, facial expression, and pose may be eliminated or reduced. Theoretical analysis shows that PCA, LDA, and LPP can be obtained from different graph models. We compare the proposed Laplacianface approach with Eigenface and Fisherface methods on three different face data sets. Experimental results suggest that the proposed Laplacianface approach provides a better representation and achieves lower error rates in face recognition. Principal Component Analysis (PCA) is a statistical method under the broad title of factor analysis. The purpose of PCA is to reduce the large dimensionality of the data space (observed variables) to the smaller intrinsic dimensionality of feature space (independent variables), which are needed to describe the data economically. This is the case when there is a strong correlation between observed variables. The jobs which PCA can do are prediction, redundancy removal, feature extraction, data compression, etc. Because PCA is a known powerful technique which can do something in the linear domain, applications having linear models are suitable, such as signal processing, image processing, system and control theory, communications, etc. The main idea of using PCA for face recognition is to express the large 1-D vector of pixels constructed from 2-D face image into the compact principal components of the feature space. This is called eigenspace projection. Eigenspace is calculated by identifying the eigenvectors of the covariance matrix derived from a set of fingerprint images (vectors).