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Proceedings ArticleDOI

Face recognition using eigenfaces

TLDR
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.
Abstract
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. This approach treats face recognition as a two-dimensional recognition problem, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. Face images are projected onto a feature space ('face space') that best encodes the variation among known face images. The face space is defined by the 'eigenfaces', which are the eigenvectors of the set of faces; they do not necessarily correspond to isolated features such as eyes, ears, and noses. The framework provides the ability to learn to recognize new faces in an unsupervised manner. >

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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.
Journal ArticleDOI

Robust Face Recognition via Sparse Representation

TL;DR: This work considers the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise, and proposes a general classification algorithm for (image-based) object recognition based on a sparse representation computed by C1-minimization.

Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments

TL;DR: The database contains labeled face photographs spanning the range of conditions typically encountered in everyday life, and exhibits “natural” variability in factors such as pose, lighting, race, accessories, occlusions, and background.
Journal ArticleDOI

Alignment by Maximization of Mutual Information

TL;DR: A new information-theoretic approach is presented for finding the pose of an object in an image that works well in domains where edge or gradient-magnitude based methods have difficulty, yet it is more robust than traditional correlation.
Proceedings ArticleDOI

PCA-SIFT: a more distinctive representation for local image descriptors

TL;DR: This paper examines (and improves upon) the local image descriptor used by SIFT, and demonstrates that the PCA-based local descriptors are more distinctive, more robust to image deformations, and more compact than the standard SIFT representation.
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

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.
BookDOI

Parallel Models of Associative Memory : Updated Edition

TL;DR: This chapter discusses G.E. Hinton's models of Information Processing in the Brain, Implementing Semantic Networks in Parallel Hardware, and R. Ratcliff's Parallel-Processing Mechanisms and Processing of Organized Information in Human Memory.
Journal ArticleDOI

From piecemeal to configurational representation of faces

TL;DR: The development at age 10 of the ability to encode orientation-specific configurational aspects of a face may reflect completion of certain maturational changes in the right cerebral hemisphere.
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.
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