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Face recognition using principle component analysis, eigenface and neural network

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TLDR
In this paper, the authors presented a methodology for face recognition based on information theory approach of coding and decoding the face image, the proposed methodology is connection of two stages - Feature extraction using principle component analysis and recognition using the feed forward back propagation Neural Network.
Abstract
Face is a complex multidimensional visual model and developing a computational model for face recognition is difficult. The paper presents a methodology for face recognition based on information theory approach of coding and decoding the face image. Proposed methodology is connection of two stages - Feature extraction using principle component analysis and recognition using the feed forward back propagation Neural Network. The algorithm has been tested on 400 images (40 classes). A recognition score for test lot is calculated by considering almost all the variants of feature extraction. The proposed methods were tested on Olivetti and Oracle Research Laboratory (ORL) face database. Test results gave a recognition rate of 97.018%.

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Citations
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Face recognition using transform domain feature extraction and PSO-based feature selection

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Influence of low resolution of images on reliability of face detection and recognition

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Brainwave-driven human-robot collaboration in construction

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References
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Journal ArticleDOI

Eigenfaces for recognition

TL;DR: A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.
Journal ArticleDOI

Application of the Karhunen-Loeve procedure for the characterization of human faces

TL;DR: The use of natural symmetries (mirror images) in a well-defined family of patterns (human faces) is discussed within the framework of the Karhunen-Loeve expansion, which results in an extension of the data and imposes even and odd symmetry on the eigenfunctions of the covariance matrix.
Journal ArticleDOI

Low-dimensional procedure for the characterization of human faces

TL;DR: In this article, a method for the representation of (pictures of) faces is presented, which results in the characterization of a face, to within an error bound, by a relatively low-dimensional vector.
Proceedings ArticleDOI

Feature extraction from faces using deformable templates

TL;DR: A method for detecting and describing the features of faces using deformable templates is described, demonstrated by showing deformable template detecting eyes and mouths in real images.
Proceedings ArticleDOI

A feature based approach to face recognition

TL;DR: A feature-based approach to face recognition in which the features are derived from the intensity data without assuming any knowledge of the face structure is presented.
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