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
Linguistic descriptors in face recognition: A literature survey and the perspectives of future development
TL;DR: This study presents a comprehensive survey of the state-of-the-art studies in linguistic descriptors and elaborate on some promising perspectives of the developments in this area.
Proceedings ArticleDOI
Face recognition using interpolated Bezier curve based representation
S. Pal,P.K. Biswas,Ajith Abraham +2 more
TL;DR: In this scheme, edges are obtained from sobelled face images, the major feature areas are segmented and principal curves are obtained by applying thinning algorithm on the features of resulting face image by using a modified thinning algorithms based on line sweep procedures.
Proceedings ArticleDOI
Low-Dose Cardiac-Gated Spect Studies Using a Residual Convolutional Neural Network
TL;DR: The results show that the proposed CNN approach can effectively suppress the overall noise level in the reconstructed myocardium and improve the spatial resolution of the left ventricular (LV) wall.
Proceedings ArticleDOI
Human face detection using color spaces and region property measures
R. Vijayanandh,G. Balakrishnan +1 more
TL;DR: This proposed paper consists of two steps, the first step is to detect the more skinned region by the fusion of RGB, YCbCr skin color region and CIEL∗ a∗ b skin labeled image using HillClimbing segmentation with K-Means clustering.
Proceedings ArticleDOI
Human identification system based on feature level fusion using face and gait biometrics
TL;DR: The achieved results showed that the integrated face and gait features carry the most discriminating power compared to any individual biometric.
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.