By: Kirti Chawla

1. By: Kirti Chawla

2. Definition Biometrics utilize ”something you are” to authenticate identification. This might include fingerprints, retina pattern, iris, hand geometry, vein patterns, voice password, or signature dynamics. 0% 25% 50% 75% 100%

3. Building Blocks • Sensor • Feature Extraction Algorithm • Search & Match Algorithm • Identity Database 0% 25% 50% 75% 100%

4. Building Blocks: Sensor A sensor that responds to biological stimulus, such as fingerprints, voice, retinas, Thumb pressure dynamics to generate signal that can be measured or interpreted. A typical sensor is solid-state device, which includes method to extract features, for which it is targeted. Fujitsu MBF 200 Solid State Fingerprint Sensor 0% 25% 50% 75% 100%

5. Building Blocks: Feature Extraction Algorithm A feature extraction algorithm detects and isolates portions of digital signal emanated out of a sensor. The signal generated by sensor contains identifying properties, which needs to be isolated and characterized. The algorithm creates descriptors or characteristic attributes on per signal basis. An identifying feature is called minutiae and is stored in a file called template. A template typically contains ~ 200 minutiae. Typical features in a fingerprint 0% 25% 50% 75% 100%

6. M M I I N N U U T T I I A A E E Building Blocks: Search & Match Algorithm A search & match algorithm takes an input characteristic feature and compares it with stored feature(s) and outputs either success or failure of the outcome. The features can be stored to facilitate optimal search strategies using deterministic bounds in range ~O(Log2(N)xLog2(M)) to ~O(NxM), where N is no. of features, M is no. of templates. 1 2 3 4 5 6 7 N 1 Minutiae belonging to a particular user 1 2 3 4 5 6 7 N M Typical storage patterns of minutiae 0% 25% 50% 75% 100%

7. Building Blocks: Identity Database An Identity database is collection of templates on which a given search & match algorithm operates to find whether a given input characteristic matches or not. There is a slight variation in the algorithms that operate upon the identity database. The design of Identity database affects the algorithms operating on it. A feature (F), when operating on a Identity database (D) can have an evaluation criteria of matching with All or majority (M). Symbolically, it can be represented as: TRUE When feature (F) matches with majority (M) of features in Identity database (D) Search_and_Match(F, A|M, D) FALSE When feature (F) does not matches with majority (M) of features in Identity database (D) Matching criterion of a feature in Identity database 0% 25% 50% 75% 100%

8. Physical Phenomenon(s) • Non-Behavioral Phenomenon(s) • Fingerprints • Retina Scans • Iris Scans • Voice Patterns • Blood vessel measurements • Behavioral Phenomenon(s) • Keying rate • Breathing rate • Mouse movements • Utterance Patterns • Signature dynamics 0% 25% 50% 75% 100%

9. Working Setup • Schematic(s) • Pseudo Code • Flow chart(s) 0% 25% 50% 75% 100%

10. Working Setup:Schematic(s) Sensor with Feature Extraction Algorithm Identity Database Learning or Enrollment Operation Sensor with Feature Extraction Algorithm Search & Match Algorithm Identity Database Verification Operation 0% 25% 50% 75% 100%

11. Working Setup:Pseudo Code Sensor Subsystem Search & Match Algorithm Identity Database 0% 25% 50% 75% 100%

12. Start Is there an Interrupt ? 1 Capture user data Extract features Make template If operation is ENROLL Send data to Identity Database Search & Match 1 Stop Working Setup:Flow chart(s) Yes No Yes No 0% 25% 50% 75% 100%

13. Applications • Security systems at pertinent places (Airports, Banks, …) • Alternative of SWIPE cards in Personnel Management • Animal breeding management … 0% 25% 50% 75% 100%

14. Q & A