Journal ArticleDOI
Face recognition
Keun-Chang Kwak,Witold Pedrycz +1 more
TLDR
This work designs classifiers based on the well-known fisherface method and demonstrates that the proposed method comes with better performance when compared with other template-based techniques and shows substantial insensitivity to large variation in light direction and facial expression.About:
This article is published in Pattern Recognition Letters.The article was published on 2005-05-01. It has received 679 citations till now. The article focuses on the topics: Facial recognition system & Fuzzy logic.read more
Citations
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Proceedings ArticleDOI
Dynamically optimizing face recognition using PCA
Zhihong Zhao,Beibei Li +1 more
TL;DR: A dynamically optimizing PCA method for face recognition is presented, and the results show that this method achieved better results compared to a number of state of the art PCA variants.
Book ChapterDOI
Trends in Nearest Feature Classification for Face Recognition - Achievements and Perspectives
TL;DR: Why face recognition researchers prefer to focus in subsequent stages of the pattern recognition system instead of in dimensionality reduction, especially in the so-called small sample size (SSS) case, one of the most busy study fields on pixel-based face recognition.
Book ChapterDOI
Assessment of Facial Recognition System Performance in Realistic Operating Environments
TL;DR: This work introduces a methodology to explore the sensitivities of a facial recognition imaging system to blur, noise, and turbulence effects and the ramifications of these results on the design of long-range facial recognition systems.
Journal ArticleDOI
Robust intelligent PCA-based face recognition framework using GNP-fuzzy data mining
TL;DR: Experimental results demonstrate that the proposed framework performs better and is more robust against noise compared with other traditional face recognition algorithms, i.e. linear discriminant analysis (LDA) and local binary patterns (LBPs).
References
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Journal ArticleDOI
Eigenfaces vs. Fisherfaces: recognition using class specific linear projection
TL;DR: A face recognition algorithm which is insensitive to large variation in lighting direction and facial expression is developed, based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variations in lighting and facial expressions.
Proceedings ArticleDOI
Face recognition using eigenfaces
Matthew Turk,Alex Pentland +1 more
TL;DR: An approach to the detection and identification of human faces is presented, and a working, near-real-time face recognition system which tracks a subject's head and then recognizes the person by comparing characteristics of the face to those of known individuals is described.
Journal ArticleDOI
Face recognition: features versus templates
Roberto Brunelli,Tomaso Poggio +1 more
TL;DR: Two new algorithms for computer recognition of human faces, one based on the computation of a set of geometrical features, such as nose width and length, mouth position, and chin shape, and the second based on almost-gray-level template matching are presented.
Journal ArticleDOI
The FERET database and evaluation procedure for face-recognition algorithms
TL;DR: The FERET evaluation procedure is an independently administered test of face-recognition algorithms to allow a direct comparison between different algorithms and to assess the state of the art in face recognition.
Proceedings ArticleDOI
View-based and modular eigenspaces for face recognition
Pentland,Moghaddam,Starner +2 more
TL;DR: In this paper, a view-based multiple-observer eigenspace technique is proposed for use in face recognition under variable pose, which incorporates salient features such as the eyes, nose and mouth, in an eigen feature layer.