This document discusses face recognition as a type of biometric identification. It describes the history and theory behind facial recognition, including eigenface technology and local feature analysis. Specific algorithms are explained, such as using neural networks to find and identify eyes, using eigenfaces with infrared images, and a scale-space approach using profile matching. Applications of facial recognition include authentication, identification, and law enforcement. The document outlines a potential project to implement face recognition profile matching using MATLAB.

1. Face Recognition
B.RANJITHA REDDY ,
ANUSHA. K

2. Introduction to Biometrics
The term "biometrics" is derived from
the Greek words bio (life) and metric
(to measure). The biometric provides
most secure level of authorization.

3. 
Types Of Biometrics
Physiological.
Behavioral
Physiological.: Physiological are related to
the shape of the body. Examples include,
fingerprint, face recognition , DNA,
hand and palm geometry , iris
recognition.

4. FINGER PRINT SPEECH RECOGNITION

5. History of
Facial Recognition
• Late 1980s: Research
• Mid 1990s: Commercialization
• Current
- Authentication
- ID
- Law Enforcement

6. Theory Behind
Facial Recognition I
• Eigenface Technology:
• Local Feature Analysis: 32-50 blocks

7. Theory Behind
Facial Recognition II
• Identalink TrueFace

8. Eye Identification Using
Neural Networks
• 2 Neural Networks
- Finding the eyes
- Identifying the person
• Small vs Large Window

9. Infrared Images and
Eigenfaces I
• Training and Test set of images
• Eigenface

10. Infrared Images
and Eigenfaces II
• Threshold Euclidean Distance

11. Scale-Space Approach
from Profiles I
• Profile Line
• Locate Nose Tip

12. Scale-Space Approach
from Profiles II
• Locate Extrema (1, 2, 4-9)
and Inflection Points (10 –12)
• Feature Vectors
• Euclidean Distance

13. Morphological Operations on
Profiles I
• 2-D shape represented by 1-D function
• Dilation • Erosion

14. Morphological Operations on
Profiles II
• 3 Shapes: A, M1, M2
• 3 feature vectors
- centroid  face
- centroid  hair
• Minimal Euclidean Distance
between 2 profile images

15. Advantages Over
Competing Systems
• Voluntary Action vs Passive Usage
• Data Acquisition
• Environment interfacing is very less.
• Cost is reasonable.

16. APPLICATIONS
Facial Recognition
Hand Geometry
Iris Scan
Retina Scan
Vascular Patterns
Finger print
DNA

17. Project Selection / Outline
• Algorithm: MATLAB implementation of
face recognition profile matching
• Database: MATLAB development of
file system
• Data Acquisition: Multimedia Lab
video camera or digital camera

18. References
• Ross Cutler, “Face Recognition Using Infrared Images and Eigenfaces”, April 1996.
• Age Eide, Christer Jahren, Stig Jorgensen, Thomas Lindblad, Clark S. Lindsey, and
Kare Osterud, “Eye Identification for Face Recognition with Neural Netowrks”, 1996.
• Zdravko Liposcak and Sven Loncaric, “Face Recognition from Profiles Using
Morphological Operators,” 1998.
• Zdravko Liposcak and Sven Loncaric, “A Scale-Space Approach to Face Recognition
from Profiles,” 1999.