Facial recognition technology uses computer vision to identify or verify a person's identity from their face. It involves capturing an image, extracting facial features, comparing the features to images in a database, and determining a match or non-match. Facial recognition has various applications in security, human-computer interaction, and law enforcement. It uses algorithms like principal component analysis and linear discriminant analysis to analyze facial features and images. The technology has improved over time but also raises privacy concerns that must be addressed.

1. INFORMATION TECHNOLOGY
0801291260 NISHANT KUMAR SINHA

2. Road Map
 Introduction to Facial Recognition Technology
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 History and Development
 Identification Procedure
 Motivation
 Implementation & Performance
 Algorithm Used

3. INTRODUCTION

4. INTRODUCTION
Facial recognition is a form of computer vision
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that uses faces to attempt to identify a person
or verify a person’s claimed identity.
Regardless of specific method used, the facial
recognition is accomplished in a five step
process.

5. HISTORY AND DEVELOPMENT

6. HISTORY & DEVELOPMENT
•Late 1980s: Research
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• Mid 1990s: Commercialization
• Current
- Authentication
- ID
- Law Enforcement

7. HISTORY & DEVELOPMENT
• September 24, 1999: OLETC  ILEFIS
- 64 facial features
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- 256 unique shapes / feature
- quicker processing, look-up time
• January 2001: Privacy Debate
- Super Bowl
- Tampa Entertainment District
• September 11, 2001: Impact on Market
- Visionics

8. HISTORY & DEVELOPMENT
•September 21, 2001: Looking Ahead
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- Colorado DMV: July 2001
- Neighborhoods (ie, Tampa Police Department)

9. IDENTIFICATION PROCEDURE

10. FACE RECOGNITION
Two types of comparison in face recognition
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1. Verification- The system compare the given individual
with who that individual says they are.
2. Identification-The system compares a given individual to
all the other individuals in the database and gives a ranked
list of matches.

11. FOUR STAGES OF IDENTIFICATION
 Capture-Capture the behavioral sample
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 Extraction-unique data is extracted from the sample and a
template is created.
 Comparison-the template is compared with a new sample.
 Match/non match-the system decides whether the new
samples are matched or not.

12. MOTIVATION

13. MOTIVATION - SECURITY
 Recognize criminals
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1. In public spaces (airports, shopping centers)
2. In stores
 Verify identity to grant access in restricted
areas: non-invasive Biometrics
1.Airports
2.Office
3.Risk: privacy rights

14. MOTIVATION–HUMAN MACHINE INTERFACE
 Government Use
1. Law enforcement
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2.Security/counterterrorism
3.Immigration
 Commercial Use
1. Cell phones (Omron, Iphone, etc)
2. Residential security
3. Voter verification
4. Banking using ATM
5. Computers
6. Intelligent buildings

15. IMPLEMENTATION & PERFORMANCE

16. IMPLEMENTATION & PERFORMANCE
IMPLEMENTATION IMPLEMENTATION
• False Acceptance Rate
•Data acquisition [FAR]
•Input processing • False Rejection Rates
[FRR]
•Face image
• Response time
classification • Decision Threshold
•Decision making • Enrollment time

17. ALGORITHM

18. PRINCIPAL COMPONENT ANALYSIS
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19. PRINCIPAL COMPONENT ANALYSIS
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20. LINEAR DISCRIMINANT ANALYSIS
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21. LINEAR DISCRIMINANT ANALYSIS
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22. CONCLUSION

23. CONCLUSION
Face recognition technologies have been associated
generally with very costly top secure applications. Today
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the core technologies have evolved and the cost of
equipments is going down dramatically due to the
integration and the increasing processing power. Certain
applications of face recognition technology are now cost
effective ,reliable and highly accurate. As a result there
are no technological or financial barriers for stepping
from the pilot project to widespread deployment.