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Introduction to Biometric Systems

Introduction to Biometric Systems. Ruomu Guo CSPC 620—Computer Security. Overview. Identification Person’s body && identity Applications: Fingerprint, Iris, Retina Recognition, Face Detection, Hand Geometry.

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Introduction to Biometric Systems

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  1. Introduction to Biometric Systems Ruomu Guo CSPC 620—Computer Security

  2. Overview • Identification Person’s body && identity • Applications: Fingerprint, Iris, Retina Recognition, Face Detection, Hand Geometry. Relationship between Biometric System and certain topics in the area of computer security.

  3. Overview • Refers to the use of mathematical statistical methods to analyze biological behaviors or characteristics. • Biometric System mainly consists of four modules—Sensor, Feature Extraction, Matcher, System Database Storage. • Difficulties: Accuracy, Speed, Resource Requirements, Harmless to human beings, Robust to fraudulent attacks.

  4. Measurement of Biometric System Fig: The relationship between FAR, FRR, and Threshold Value

  5. Fingerprint Recognition • Fingerprint Recognition, because of its lifetime invariance, uniqueness and convenience, is becoming an important method for biometric identification. • Finger skin ridge and valley forms a regular array of different pattern types. Valley, ridge combined with point and bifurcation point, are called the fingerprint minutiae points (minutiae). • By comparing different fingerprint minutiae, person’s identity can be recognized or identified.

  6. Fingerprint Recognition Fig: Block Diagram of Fingerprint Recognition Processes

  7. Fingerprint Recognition • Sense: off-line fingerprint acquisition, live-scan sensing. • Feature Extraction: singular region, local ridge orientation • Matching: Correlation-based matching, Minutiae-based matching, Ridge feature-based matching • Database Storage: update periodically

  8. Face Detection • Face Detection is also a popular method of biometric system for recognition and identifies individual’s identity. • Advantages: widely accept as a identifier, least intrusive. • Disadvantages: illumination, disguise for circumvention, and incompatible with pure identification protocol.

  9. Face Detection • Primary methods for detecting faces 1. Knowledge-based methods 2. Feature invariant approaches 3. Template matching methods 4. Appearance-based methods

  10. Face Detection • The technique for face recognition can be classified as following three groups: 1. Feature Methods: 2. Holistic Methods: 3. Hybrid Methods:

  11. PCAApplication • PCA (Principal Component Analysis) A face image usually defines a point in the high-dimensional image space. PCA is used to simplify the required process of analysis by reducing the dimensional spaces or subspaces.

  12. Conclusion • Biometric System is not independent as a module for entire computer security area. • Some ticklish problems in computer security will be solved appropriately such as authentication for each person’s identity before they will enter or access to other systems. • Scientists are still trying to exploit other methods to improve the performance of biometric system with more enhancement of computer security.

  13. Reference • 1. A. K. Jain, A. Ross and S. Prabhakar, An Introduction to Biometric System IEEE Transactions on Circuits and Systems for Video Technology, Special Issue on Image- and Video-Based Biometrics, Vol. 14, No. 1, pp. 4-20, January 2004. • 2. A. Jain, R. Bolle, and S. Pankanti, Introduction to Biometrics: Personal Identification in Networked Society (A. Jain, R. Bolle, and S. Pankanti, Eds. ), pp. 1-41, Boston, MA: Kluwer Academic, 1999. • 3. D. Maltoni. A tutorial on Fingerprint Recognition: In M. Tistarelli, J. Bigun, and E. Grosso, editors, Biometrics School 2003, LNCS 3161, pages 43-68. Springer Verlag, Berlin, Heidelberg, 2005. • 4. Description of Face Detection at Wikipedia: http://en.wikipedia.org/wiki/Face_detection.

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