This document describes a face recognition system created by three students from Vietnam National University, Hanoi. The system uses a PDBNN algorithm for face detection and recognition. It first applies discrete Fourier transforms to input images before using neural network filters and merging to detect faces. Eye localization then precisely finds eyes before facial features are extracted using principal component analysis, Fisher's linear discriminant, or local feature analysis. The extracted feature vectors are input to face recognition to identify individuals. Potential applications of the system include security, access control, surveillance, photo tagging, and digital photography.

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

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

3. ĐẠI HỌC
CÔNG NGHỆ Outline
 Introduction
 Architecture
 Discrete Fourier Transform (DFT)
 Face Detection
 Eye Localization
 Facial Feature Extraction
 Face Recognition
 Applications
 Conclusion
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4. ĐẠI HỌC
CÔNG NGHỆ
Introduction
Are they the
same
people?
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5. ĐẠI HỌC
CÔNG NGHỆ Architecture
Figure 1: System configuration of the PDBNN face recognition system
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6. ĐẠI HỌC
CÔNG NGHỆ
Discrete Fourier Transform (DFT)
Transformation of input images from time domain to frequency domain
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7. ĐẠI HỌC
CÔNG NGHỆ
Discrete Fourier Transform (DFT)
Fourier Transform in 2 – D images
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8. ĐẠI HỌC
CÔNG NGHỆ
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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9. ĐẠI HỌC
CÔNG NGHỆ
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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10. ĐẠI HỌC
CÔNG NGHỆ
Face Detection
Face Detection consists of two stages:
• Neural – Network Filters
• Merging overlapping detection
Figure 2: The basic algorithm used for face detection.
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11. ĐẠI HỌC
CÔNG NGHỆ
Face Detection
The result of face detection:
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12. ĐẠI HỌC
CÔNG NGHỆ
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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13. ĐẠI HỌC
CÔNG NGHỆ
Facial Feature Extraction
• Facial feature extraction techniques can be classified into two
categories are feature – invariant approaches and template –
based approaches
Facial Feature Extraction
Feature – Invariant Template-Based
Approaches Approaches
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14. ĐẠI HỌC
CÔNG NGHỆ
Facial Feature Extraction
• Feature – 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 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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15. ĐẠI HỌC
CÔNG NGHỆ
Facial Feature Extraction
One important issue for statistical template matching is the curse
of dimensionality.
Principal Component Analysis
(PCA)
Fisher's Linear Discriminant
Efficient Extraction (PLD)
Local Feature Analysis
(LFA)
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16. ĐẠI HỌC
CÔNG NGHỆ
Face Recognition
Accept
Feature Face
Vectors Recognition
Reject
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17. ĐẠI HỌC
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Applications
Sercurity
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18. ĐẠI HỌC
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Applications
Access control Surveillance
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19. ĐẠI HỌC
CÔNG NGHỆ
Applications
Photo tagging in Social Network Digital Photography
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20. ĐẠI HỌC
CÔNG NGHỆ
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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21. ĐẠI HỌC
CÔNG NGHỆ
Thank you & FAQs