Perfect histogram matching PCA for face recognition

Sevcenco, Ana-Maria; Lu, Wu-Sheng

doi:10.1007/s11045-009-0099-y

Cited by 5 publications

(3 citation statements)

References 27 publications

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“…(3) Reducing dimensionality of the facial feature sets extracted through AAM with PCA [3]. (4) To get simulating results though Piecewise Facial Aging Model.…”

Section: Model Constructing 21 Constructing Model and Flow Chartmentioning

confidence: 99%

“…Features could be extracted through AAM. Considering the texture features, to gray the color images could reduce the computation cost without affection to the real aging result; L elimination the affection of illumination to facial texture; To ensure gray uniformed distribution through the histogram matching algorithm [4].…”

Section: Preprocessing Of the Human Facial Picturementioning

confidence: 99%

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A Study of Recognition about Human Face

Yang¹,

Yu²,

Xiao³

et al. 2017

JMI

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This paper studied the simulation problems of facial aging with the computer visual processing software. According to comparing the most used facial pictures, this paper decided to take the face images from FE-NET database as the objects of modeling analysis. Considering the AAM, WT and other Facial Feature Extraction Algorithms, this took the AMM algorithm to extract and mark the facial feature, gray and match histogram. Actually, the high dimensionality of the extracted feature has a bad affection on efficiency and result in post processing, so this paper adopts the classical method PCA to process the facial feature set. This paper adopts a piecewise-linear algorithm to segment all facial images based on ages to four sections, and extract features of every section to make linear similarity compare. This paper simulates images and reverse simulation in every section, with good results.

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“…(3) Reducing dimensionality of the facial feature sets extracted through AAM with PCA [3]. (4) To get simulating results though Piecewise Facial Aging Model.…”

Section: Model Constructing 21 Constructing Model and Flow Chartmentioning

confidence: 99%

Section: Preprocessing Of the Human Facial Picturementioning

confidence: 99%

A Study of Recognition about Human Face

Yang¹,

Yu²,

Xiao³

et al. 2017

JMI

View full text Add to dashboard Cite

show abstract

“…The Principle Component Analysis(PCA) algorithm often used in face recognition [6] is based on eigenface which characterize the global variation among the face images involved. PCA can represent the image only using a small number of parameters and can reduce the dimensional complexity.…”

Section: Feature Extractingmentioning

confidence: 99%

Attendance System Applied in Classroom Based on Face Image

Cao¹,

Li²

2015

Proceedings of the 2015 International Symposium on Computers &Amp; Informatics

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Aiming at the shortcomings in existing attendance mode applied in classroom, a new attendance system in classroom based on face image is proposed in this paper. This system includes face detection and face recognition. The rationale of this system is introduced in brief, and the algorithms of face detection and recognition used in this system are explained in detail. Experimental results show that the system is efficient.

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Radiometric Normalization

Mitchell¹

2012

Data Fusion: Concepts and Ideas

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Perfect histogram matching PCA for face recognition

Cited by 5 publications

References 27 publications

A Study of Recognition about Human Face

A Study of Recognition about Human Face

Attendance System Applied in Classroom Based on Face Image

Radiometric Normalization

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