Your SlideShare is downloading. ×
  • Like
13.face recog
Upcoming SlideShare
Loading in...5

Thanks for flagging this SlideShare!

Oops! An error has occurred.


Now you can save presentations on your phone or tablet

Available for both IPhone and Android

Text the download link to your phone

Standard text messaging rates apply


  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads


Total Views
On SlideShare
From Embeds
Number of Embeds



Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

    No notes for slide


  • 1. Face Recognition B.RANJITHA REDDY , ANUSHA. K
  • 2. Introduction to BiometricsThe term "biometrics" is derived fromthe Greek words bio (life) and metric(to measure). The biometric providesmost secure level of authorization.
  • 3.  Types Of Biometrics Physiological.BehavioralPhysiological.: Physiological are related to the shape of the body. Examples include, fingerprint, face recognition , DNA, hand and palm geometry , iris recognition.
  • 5. History ofFacial 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 Distancebetween 2 profile images
  • 15. Advantages OverCompeting 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.