1 Welcome Today –Admin –Module overview –Some signals CA614 Biometrics

2 Admin Lectures –John McKenna, Office: 2.47, Tel. (700)5507 –Alistair Sutherland, Lecturer –Monday 11pm, Q120 –Wednesday 9am, X130 Labs –Monday 4-6pm, LG.01 –Andrew Errity, Tutor –Michelle Tooher, Tutor

3 Admin Prerequisites –Some maths probability, linear algebra (matrices), complex numbers –Ability to program –Open mind –Please come see me if you have doubts about prerequisite knowledge

4 Admin Assessment –Continuous Assessment: 30% Assignment: Speaker/Face/Signature Verification –End of module exam: 70% Books, etc –See Module Descriptor for list –No book purchase necessary –Recommended –Headset required Composite (with microphone) recommended Sharing feasible & recommended

5 What is Biometrics? “Life measurement” “ Biometric technologies are automated methods of verifying or recognising the identity of a living person based on a physical or behavioural characteristic”, –Ben Miller, 1987

6 Projections I Frost & Sullivan (1990)

7 Projections II IBG Forecast (2000)

8 Projections III IBIA Forcast (2000)

9 Module Aims –To cover all modern approaches to biometrics, in the context of automatic computerised methods of identifying an individual based on who they really are - using a variety of attributes such as finger-prints, iris scans etc. Module Overview

10 Indicative Syllabus I Generic mathematical techniques for pattern recognition and digital filtering. Frequency domain analysis - Fourier and other transforms. Digital signal processing algorithms. Benefits of biometrics to identification systems. Enrolment and template creation. Accuracy in Biometric systems. False match rates and false non-match rates. Derived metrics

11 Indicative Syllabus II Fingerprint scan. Image acquisition and processing. Competing technologies. Finger scan problems. Facial scan. Image acquisition and processing. Iris scan. Image acquisition and processing. Voice Scan. Speech processing. Implementation of speech processing algorithms. Other physiological biometrics - signature and key- stroke scanning. Multifactor identification. Privacy risks of biometrics.

12 –Awareness of the practicability and applicability of modern method of biometrics. –That an individual's identity can be ascertained to a very high degree of confidence using appropriate sensors and systems. –The common mathematics of pattern recognition that underpins this technology. –An understanding of signals: waveforms & spectra –Competence at implementing verification algorithms –Ability to program MATLAB scripts –Ability to use HTK (Hidden Markov Model Toolkit) for verification design Learning Outcomes

13 Module Overview Achieving the aims of the module will involve the following: –Communication skills –Group Work skills –Organisational skills –Personal skills –Problem solving skills –Programming skills –Information Technology skills Cross-disciplinary partnerships –Skills transfer –Module mailing list Discussion forum; No code!!!

14 A Taste of Things to Come Today’s Lab –Speech Waveforms & Spectra –Matlab Intro Now –Demos Next –Authentication technologies

15 Rough Plan Intro/Background Maths Technologies Issues