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Introduction to Biometrics

This lecture covers various biometric methods such as fingerprint identification, hand geometry, face location, and multi-biometrics. It delves into image feature extraction, fingerprint matching techniques, hand geometry authentication, face location in images, and the integration of multiple biometric factors for enhanced identification. The lecture also discusses the importance of biometrics in applications like criminal identification, ATM security, and e-commerce. Explore the diverse techniques and applications of biometrics in this comprehensive overview.

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Introduction to Biometrics

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  1. Introduction to Biometrics Dr. Bhavani Thuraisingham The University of Texas at Dallas Lecture #6 Guest Lecture + Some Topics in Biometrics September 12, 2005

  2. Outline • Guest Lecture • Some Topics in Biometrics

  3. Introduction to Biometrics Guest Lecture Image Feature Extraction and Annotation September 12, 2005

  4. Some Topics in Biometrics • Reference • http://biometrics.cse.msu.edu/info.html • Papers published on the web by researchers at Michigan State University • Overview • Fingerprint Identification • Hand Geometry • Face Location • Multi-Biometrics

  5. Overview • Biometrics is the automatic identification of a person based on his/her physiological and behavioral characteristics • Verification vs. Identification • Am I whom I claim I am? • Who am I? • Applications • Criminal identification, ATMs, Cellular Phones, Smart cards, PCs, E-Commerce, Automobiles (biometrics replacing car keys)

  6. Fingerprint Identification • Finger-print matching • Two categories: Minutiae based, Correlation based • Minutiae-based techniques • First find the minutiae points and then map their relative placement on the finger • Issues: difficult to extract minutiae points if fingerprinting is of low quality • Correlation-based techniques • Spatial correlation of regions • Issues: Affected by Image translation

  7. Fingerprint Identification (Concluded)) • Finger-print classification • Classify the fingerprints so that search time is reduced • Form groups of fingerprints; Classification is obtained by matching with pre-specified types of finger-prints • When a new finger-print arrives try and place it into a group • Classification based data mining/machine learning algorithms such as K-Nearest Neighbor • Fingerprint Image Enhancement • Algorithms to enhance the finger-print • This is expected to facilitate finger-print matching • Makes it less difficult to extract minutiae from fingerprints

  8. Hand Geometry • Uses geometric shape of hand for authenticating user’s identity • Combine various individual features of hand for effective verification • Human hand is not in general unique (not the case with fingerprints) • Reason one may want to use hands instead of finger-prints is to ensure privacy • Some pros and cons • Hand geometry gives better privacy • But hand geometry is not unique; therefore may be used for verification • Not suitable for identification

  9. Face Location and Retrieval • Problem • Given an arbitrary black and white still image, find the location and size of every human face it contains • Applications • First step in automatic face recognition • Image database indexing • Search by content for surveillance systems

  10. Multi-Biometrics • Integrating Faces and Fingerprints for Identification • Single biometric may not be effective • Integrate multiple biometrics such as fingerprints and faces • Fingerprint, Face and Speech • Better to use a third biometric and that is speech • Challenge • What is an effective combination of biometrics?

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