The document discusses various biometric techniques for identifying individuals, including fingerprint recognition, hand geometry analysis, iris scanning, retinal pattern recognition, facial recognition, signature analysis, and speaker verification. It provides a brief history of biometrics and describes the basic components of a biometric system. Fingerprint recognition is described as the oldest technique, examining ridge and furrow patterns. Factors like pressure and skin condition affect fingerprint scanning accuracy. Multimodal biometrics that combine techniques can provide more reliable identification. Biometrics have various applications and also some drawbacks depending on the specific technique used.

1. PATTERN MATCHING USING BIOMETRIC TECHNIQUES For Documentation and Downloads Visit

2. INTRODUCTION Bio(Life), Metrics(Measure) Biometrics can be defined as the science and technology of measuring and statistically analyzing biological data. Biometrics allow us to authenticate ourselves with things that we carry with us wherever we may go, such as our hands, eyes, voices, faces, fingerprints etc.

3. HISTORY British scientist Sir Francis Galton proposed the use of fingerprints IN 19 TH Century. One of the most well-known biometrics characteristics is fingerprint. Identification systems available from 1990’s.

4. BIOMETRIC SYSTEM CHARACTERISTIC Universal Unique Permanent Collectable

5. Basic architecture of a biometric system

6. DIFFERENT METHODOLOGIES Finger printing recognition Hand geometry Iris based identification Retinal pattern recognition Facial recognition Signature recognition Speaker verification

7. FINGER PRINT IDENTIFICATION Finger print technique is the Oldest of all the Biometric Techniques. It is a pattern of Ridges and Furrows on surface of Finger-tip. Finger prints are unique and remains Unchanged.

8. HAND GEOMETRY Examines : hand shape, lengths and widths of the fingers, and the overall size of the hand. This technique is simple, relatively easy to use, and inexpensive.

9. IRIS-BASED IDENTIFICATION Each Iris is Unique. It is scanned by a low intensity light source.

10. RETINAL PATTERN RECOGNITION It is the innermost layer of the eye. The pattern formed by veins beneath the surface of the retina is unique. A low-intensity beam of visible or infrared light into a person’s eye

11. FACIAL RECOGNITION Facial scanning involves scanning of the entire face and checking of critical points. Generally used in association with other biometric techniques. Applied for both still photographs & Video tape or movies.

12. SIGNATURE RECOGNITION Two approaches, 1.Static signature verification. (determines only geometric features) 2.Dynamic signature verification. ( determines both geometric features and dynamic features)

13. SPEAKER VERIFICATION Depends on the frequency, stress and accent of speech. The main physiological aspect of the human speech production system is the vocal tract shape.

14. FINGER PRINT ANALYSIS Two categories: 1.Minutiae-based 2.Correlation based Fig. Finger print matching

15. FINGERPRINT PATTERN TYPES Loop Whorl . Arch

16. FACTORS CONSIDERED IN SCANNING Amount of pressure applied to the Fingertip . The presence of any cuts or other deformities on the fingertip. The dryness of the skin, edges of rides. Ridge bifurcations, or branches—collectively known as minutiae

17. MULTIBIOMETRICS Integrating Faces and Fingerprints for Personal Identification. A Multimodal Biometric System Using Fingerprint, Face, and Speech.

18. APPLICATIONS Prison visitor systems Driver’s licenses Canteen administration in a campus Benefit payment systems Border Control Voting systems Future Applications : ATM machine use Workstation and network access Travel and tourism Internet transactions Telephone transactions

19. DRAWBACKS OF BIOMETRIC METHODS RETINAL SCAN IRIS RECOGNITION FINGER IMAGING HAND GEOMETRY FACIAL RECOGNITION VOICE RECOGNITION SIGNATURE RECOGNITION

20. CONCLUSION The coming years are going to witness is giant expansion in hardware software changing conventions of speed on a much broader spectrum. Biometric systems are going to play a major role in coming future mostly in Security field.

21. REFERENCES [Bigun, 1997] Bigun, E.S., J. Bigun, Duc, B.: “Expert conciliation for multi modal person authentication systems by Bayesian statistics,” In Proc. 1st Int. Conf. On Audio Video-Based Personal Authentication [Brunelli et al, 1995] R. Brunelli, and D. Falavigna, "Personal identification using multiple cues," IEEE Trans. on Pattern Analysis and Machine Intelligence. [Dieckmann]Dieckmann, U.Plankensteiner, P., and Wagner, T.: “SESAM: A biometric person identification system using sensor fusion,” In Pattern Recognition Letters, Vol. 18, No. 9

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