Face recognition system

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VIETNAM NATIONAL UNIVERSITY, HANOI VIETNAM NATIONAL UNIVERSITY, HANOI University of Engineering & University of Engineering & Technology Technology ĐẠI CÔNG HỌC NGHỆ Face Recognition Face Recognition System System Members: Van-Ly Nguyen Van-Ly Nguyen Van-Khai Ngo Van-Khai Ngo Ngoc- Ngoc- Sinh Nguyen Sinh Nguyen

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Transcript of Face recognition system

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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

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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

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HỌCNGHỆ Outline

IntroductionIntroductionArchitectureArchitecture

Discrete Fourier Transform (DFT)Face DetectionEye LocalizationFacial Feature ExtractionFace Recognition

ApplicationsApplicationsConclusionConclusion

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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

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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

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Sercurity

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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.

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Thank you & FAQs Thank you & FAQs