Book ChapterDOI
Face Recognition Using Weighted Modular Principle Component Analysis
Arvind Kumar,Sukhendu Das,V. Kamakoti +2 more
- pp 362-367
TLDR
A method of face recognition using a weighted modular principle component analysis (WMPCA) has a better recognition rate, when compared with conventional PCA, for faces with large variations in expression and illumination.Abstract:
A method of face recognition using a weighted modular principle component analysis (WMPCA) is presented in this paper. The proposed methodology has a better recognition rate, when compared with conventional PCA, for faces with large variations in expression and illumination. The face is divided into horizontal sub-regions such as forehead, eyes, nose and mouth. Then each of them are separately analyzed using PCA. The final decision is taken based on a weighted sum of errors obtained from each sub-region.A method is proposed, to calculate these weights, which is based on the assumption that different regions in a face vary at different rates with expression, pose and illumination.read more
Citations
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Journal ArticleDOI
A Survey of Face Recognition Techniques
Rabia Jafri,Hamid R. Arabnia +1 more
TL;DR: A discussion outlining the incentive for using face recognition, the applications of this technology, and some of the difficulties plaguing current systems with regard to this task has been provided.
Support Vector Machines for Face Recognition
N. Prakash,Yashpal Singh +1 more
TL;DR: This paper tries to give an idea of the state of the art of face recognition technology and mentions some advantages and disadvantages of the Support Vector Machines and their resolution.
Journal ArticleDOI
Face recognition based on eigen features of multi scaled face components and an artificial neural network
TL;DR: The basic idea of the proposed method is to construct facial feature vector by down-sampling face components such as eyes, nose, mouth and whole face with different resolutions based on significance of face component, and then subspace Principal Component Analysis (PCA) or Linear Discriminant Analysis (LDA) method is employed to acquire a good representation of facial features.
Proceedings Article
Multiple viewpoints based overview for face recognition
TL;DR: A comprehensive survey on face recognition from practical applications, sensory inputs, methods, and application conditions, and a comprehensive survey of face recognition methods from the viewpoints of signal processing and machine learning.
Journal ArticleDOI
Class-wise two-dimensional PCA method for face recognition
TL;DR: A novel class-wise two-dimensional principal component analysis (PCA)-based face recognition algorithm that can successively detect and recognise faces in not only images but also in video files is presented.
References
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Journal ArticleDOI
Eigenfaces for recognition
Matthew Turk,Alex Pentland +1 more
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
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.
Journal ArticleDOI
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TL;DR: In this paper, the authors provide an up-to-date critical survey of still-and video-based face recognition research, and provide some insights into the studies of machine recognition of faces.
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.
Proceedings ArticleDOI
Face recognition with support vector machines: global versus component-based approach
TL;DR: A component-based method and two global methods for face recognition and evaluate them with respect to robustness against pose changes are presented and the component system clearly outperformed both global systems on all tests.