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Face recognition system
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Transcript of Face recognition system
VIETNAM NATIONAL UNIVERSITY, HANOIVIETNAM NATIONAL UNIVERSITY, HANOI
University of Engineering & TechnologyUniversity of Engineering & TechnologyĐẠI
CÔNGHỌC
NGHỆ
Face Recognition SystemFace Recognition System
Members: Van-Ly NguyenVan-Ly Nguyen Van-Khai NgoVan-Khai Ngo
Ngoc-Sinh NguyenNgoc-Sinh Nguyen
VIETNAM NATIONAL UNIVERSITY, HANOIVIETNAM NATIONAL UNIVERSITY, HANOI
University of Engineering & TechnologyUniversity of Engineering & TechnologyĐẠI
CÔNGHỌC
NGHỆ
Face Recognition SystemFace Recognition System
Members: Van-Ly NguyenVan-Ly Nguyen Van-Khai NgoVan-Khai Ngo
Ngoc-Sinh NguyenNgoc-Sinh Nguyen
ĐẠICÔNG
HỌCNGHỆ Outline
IntroductionIntroductionArchitectureArchitecture
Discrete Fourier Transform (DFT)Face DetectionEye LocalizationFacial Feature ExtractionFace Recognition
ApplicationsApplicationsConclusionConclusion
35/23/12 Signals and SystemsSignals and Systems
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Introduction
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Are they the same
people?
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HỌCNGHỆ Architecture
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Figure 1: System configuration of the PDBNN face recognition system
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Discrete Fourier Transform (DFT)
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Transformation of input images from time domain to frequency domain
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Discrete Fourier Transform (DFT)
Fourier Transform in 2 – D images
Signals and SystemsSignals and Systems 7775/23/12
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Discrete Fourier Transform (DFT)
Low frequencies contain much more information which is suitable for recognition than higher ones
Low frequencies are likely to be found at four corners of image spectrum
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Discrete Fourier Transform (DFT)
Coefficient selection is one of the most important parameters in any recognition technique.
Some coefficient selection methods: Low frequency coefficient selection methods Square selection methods Curcular selection methods
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Face Detection
Face Detection consists of two stages:
1. Neural – Network FiltersNeural – Network Filters
2. Merging overlapping detection Merging overlapping detection
Figure 2: The basic algorithm used for face detection.
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Face Detection
The result of face detection:
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Eye Localization
Eye Localization is activated when face detection has found face
in the input image.
Since the purpose of eye localization is to normalize facial patterns into a format the recognizer can accept, eye locations need to pinpoint with much higher precision than face location
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Facial Feature Extraction
Facial feature extraction techniques can be classified into two categories are feature – invariant approaches and template – based approaches
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Facial Feature ExtractionFacial Feature Extraction
Feature – InvariantFeature – InvariantApproachesApproaches
Template-BasedTemplate-BasedApproachesApproaches
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Facial Feature Extraction
Feature – Invariant ApproachesFeature – Invariant Approaches
This type of algorithm looks for structural features that exist even when the pose, viewpoint, or lighting condition vary
The system can use various features including width of the head; distance from eyes to eyes, top of the head to eyes, eyes to the nose; and distance from eyes to the mouth
Template – based approachesTemplate – based approaches
The algorithm designs one or several standard face templates (usually frontal face template) either manually or by learning from examples in the image database
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Facial Feature Extraction
One important issue for statistical template matching is the curse of dimensionality.
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Efficient ExtractionEfficient Extraction
Principal Component Analysis Principal Component Analysis (PCA)(PCA)
Fisher's Linear Discriminant Fisher's Linear Discriminant (PLD)(PLD)
Local Feature AnalysisLocal Feature Analysis(LFA)(LFA)
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Face Recognition
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FeatureVectors
Face Face RecognitionRecognition
AcceptAccept
RejectReject
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Applications
Signals and SystemsSignals and Systems5/23/12
Sercurity
17
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Applications
Access control
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Surveillance
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Applications
Photo tagging in Social Network Digital Photography
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Conclusion
The System uses the PDBNN algorithm Face detection and Eye localization are two very
important parts in the system PDBNN can perform these two processes at very
high accuracy, more effective than other algorithms
The system is more and more popular.
Signals and SystemsSignals and Systems5/23/12 20
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Thank you & FAQs Thank you & FAQs