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Open AccessJournal Article

A survey of face detection, extraction and recognition

Yongzhong Lu, +2 more
- 01 Jan 2003 - 
- Vol. 22, Iss: 2, pp 163-195
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TLDR
The goal of this paper is to present a critical survey of existing lite- ratures on human face recognition over the last 4-5 years.
Abstract
The goal of this paper is to present a critical survey of existing lite- ratures on human face recognition over the last 4-5 years Interest and research activities in face recognition have increased significantly over the past few years, especially after the American airliner tragedy on September 11 in 2001 While this growth largely is driven by growing application demands, such as static matching of controlled photographs as in mug shots matching, credit card verification to surveillance video images, identification for law enforcement and authentication for banking and security system access, advances in signal analysis techniques, such as wavelets and neural networks, are also important catalysts As the number of proposed techniques increases, survey and evaluation becomes important

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Citations
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Face recognition across pose: A review

TL;DR: A critical survey of researches on image-based face recognition across pose is provided, classified into different categories according to their methodologies in handling pose variations, and several promising directions for future research have been suggested.
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Review of Face Detection Systems Based Artificial Neural Networks Algorithms

TL;DR: A general review of face detection studies and systems which based on different ANN approaches and algorithms and the strengths and limitations of these literature studies and system were included.
Book ChapterDOI

A Comprehensive Review on Face Recognition Methods and Factors Affecting Facial Recognition Accuracy

TL;DR: A point-by-point outline of some imperative existing strategies which are accustomed to managing the issues of face recognition has been introduced along with their face recognition accuracy and the factors responsible to degrade the performance of the study.
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Review of Face Detection Systems Based Artificial Neural Networks Algorithms

TL;DR: In this article, a general review of face detection studies and systems which based on different ANN approaches and algorithms are included also the strengths and limitations of these literature studies and system were included also.
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Color-based object segmentation method using artificial neural network

TL;DR: This study clearly shows how a novel method for fusion of the existing color spaces produces better results in practice than individual color spaces.
References
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Journal ArticleDOI

Statistical pattern recognition: a review

TL;DR: The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.
Proceedings ArticleDOI

Face recognition using eigenfaces

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

From few to many: illumination cone models for face recognition under variable lighting and pose

TL;DR: A generative appearance-based method for recognizing human faces under variation in lighting and viewpoint that exploits the fact that the set of images of an object in fixed pose but under all possible illumination conditions, is a convex cone in the space of images.
Journal ArticleDOI

Neural network-based face detection

TL;DR: A neural network-based upright frontal face detection system that arbitrates between multiple networks to improve performance over a single network, and a straightforward procedure for aligning positive face examples for training.
Journal ArticleDOI

PCA versus LDA

TL;DR: In this article, the authors show that when the training data set is small, PCA can outperform LDA and, also, that PCA is less sensitive to different training data sets.
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