PPT ON FACE RECOGNITION TECHNOLOGY

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Face Recognition Technology Presentation Transcript: 
1.Face Recognition

2.Introduction to Biometrics
The average adult working in a large business has 12 passwords to remember, and spends nearly a week in every year logging into systems.
The average cost to a large company for every password lost is $16.

3.Biometric Systems I
Fingerprint Recognition
Speech Recognition

4.Biometric Systems II

5.Biometric Systems III
Face Recognition
Multiple Biometrics

6.History of Facial Recognition

7.History: Current Times I
 September 24, 1999: OLETC ? ILEFIS
    - 64 facial features
    - 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: Current Times II
“ The mood in this country has changed dramatically in just seven days. Until last week we were trying to expand people’s privacy against incursions from the government. Now we might have to fight for what we already have.” 
State Senator Ken Gordon, D-Denver, Chairman of the  
Senate Judicial Committee
 September 21, 2001: Looking Ahead
    - Colorado DMV: July 2001
    - Neighborhoods (ie, Tampa)

9.Facial Recognition Market Outlook
 Physical Access Control
    - 5 years 
    - casinos, immigrants      at border crossings
 Computer User Access Control

10.Advantages Over Competing Systems
Voluntary Action vs Passive Usage
 Data Acquisition
    - 5% cannot provide good fingerprint 
    - environmental interference
 Cost
    - Iris Detection (movement)

11.Theory Behind Facial Recognition I

12.Theory Behind Facial Recognition II

13.Current Commercial Products

14.Eye Identification Using Neural Networks

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

Source: Power Point Presentations

PPT ON FACE RECOGNITION TECHNIQUES

PPT On Face Recognition Techniques
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Face Recognition Techniques Presentation Transcript:
1.A Review On Face Recognition Techniques

2.Contents
Introduction
Review on the topic
Related Issues
Face Recognition Techniques
Comparison
Discussion
Databases
Conclusion
References

3.Introduction
Face recognition is a biometric technique for automatic identification.

It makes use the most usual human identifier, face and unique facial characteristics.

Emergence has been fuelled due to wide range of law and commercial enforcement. 

Widely used in commercial systems to perform real-time face detection, image registration and image matching.

4.Technologies in face recognition have been evolving through years. Its importance has recently grown in a significant manner due to:
Increased civilian and commercial research projects.
Need for surveillance in trafficking.
Increased terrorist activities.
Enhanced real time computation and exploration of real time hardware.

5. Review
Before the middle 90’s, the research attention was only focused on single-face recognition. The approaches included:
Kirby and Sirovich [1] were among the first to apply principal component analysis (PCA) to face images. 
Turk and Pentland popularized PCA for face recognition [4] via eigen faces.
Neural networks have been widely applied in pattern recognition as achieve better performance than the simple minimum distance classifiers [16]. 
Using skin color etc.

6.During the past ten years, considerable progress has been made in multi-face recognition area, includes: RBF neural networks process implemented in helps in structure determination of the radial basis function (RBF) neural networks.
Discrete cosine transform (DCT) [3, 14].
Support vector machine (SVM) by Osuna et al. (1997).
Hidden Markov Model [19, 39].
Multilinear PCA [31, 32].
Face Recognition using Texture and Depth Information[38].

7.Technical approaches to face recognition
Feature based approach : based on shape and geometrical relationships of key facial features including eyes, mouth, nose, chin and curvature based face components [18]. These are more robust against rotation, scale, and illumination variations
Holistic approach (Template matching approach) : takes the input face images globally and extract important facial features based on the high-dimensional intensity values of face images automatically. They greatly rely on the accuracy of facial feature detection
Hybrid approach : uses both the face images together with the local features for face recognition.

8.Issues faced by face recognition techniques
Image intensity and orientation
Pose
Structural components   
Occlusion
Image quality
Facial expression
Illumination   

9.Face Recognition Techniques
The incredible human intelligence can be demonstrated by its ability to recognize human faces. 

Over the last three decades researches have been going on to study this outstanding visual perception of human beings in machine recognition of faces. 

While coping up with the challenges in face recognition numerous techniques have been implemented and few are as follows

10.Principal component analysis (PCA) is a statistical dimensionality reduction method. 
This subspace projection technique has found application in fields such as face recognition, pattern recognition and image compression. 
It is computationally efficient to compare images in subspaces with significantly reduced dimensions. 
PCA helps to reduce image vectors with 65,536 pixels (256x256) might be projected into a subspace with only 100 to 300 dimensions. 
PCA reveals the most effective low dimensional structure of facial patterns by decomposing the face structure into orthogonal (uncorrelated) components known as eigenvectors and eigenvalues [20]. 

Source: Power Point Presentations