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
A privacy-preserving crowd movement analysis by k-member clustering of face images
TL;DR: An experimental result demonstrates the applicability of the secure framework in capturing crowd movement characteristics even if individual features are k-aonymized so that each individual is not distinguishable from others k - 1 ones.
Proceedings ArticleDOI
Notice of Violation of IEEE Publication Principles Neural network based intelligent local face recognition using local pattern averaging
TL;DR: In this paper, N.Vivekanandan Reddy, D.Abhilash Krishna, P.Sharath Reddy and R.Shirisha have been found to be in violation of IEEE's Publication Principles.
Journal ArticleDOI
Multiple scale neural architecture for face recognition
David González-Ortega,Francisco Javier Díaz-Pernas,M. Antón-Rodríguez,Mario Martínez-Zarzuela,Isabel de la Torre-Díez,D. Boto-Giralda,J. F. Díez-Higuera +6 more
TL;DR: This paper presents a multiple scale neural architecture for face recognition composed of several stages: face detection, Difference of Gaussians, Gabor filter bank, Principal Component Analysis, and two-stage MLPs.
Book ChapterDOI
A Multi-Stage Classifier for Face Recognition Undertaken by Coarse-to-fine Strategy
Jiann-Der Lee,Chen-Hui Kuo +1 more
TL;DR: A successful face recognition system should be robust under a variety of conditions, such as varying illuminations, pose, expression, and backgrounds, and the multi-classifier systems such as local and global face information fusion are proposed in parallel process of different features or classifiers.
Book ChapterDOI
Face Verification Using SVM: Influence of Illumination
TL;DR: Influence of illumination conditions in face verification using support vector machines (SVM) and k-nearest neighbours is analysed using an experimental set up in which images are acquired in controlled or uncontrolled illumination conditions.
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.