Biometric Security QUTLINE ● Introduction Lecture 8 Face Recognition System Face Detection Location Traditional Uni-Modal Face normalization Face Recognition Feature Extraction Recognition Face Recognition Application Face Recognition Problems Introduction(1) Face 口 Current state k Face is the most common biometrics. Using the whole ce for automatic identification is a complex task ecause its appearance is constantly changing Introduction k One effective approach may employ rule-based logic and a neural network for the image classification process. The first face system is introduced in 1992. 口 Feature Set Size of to mouth, middle of cheek, size of mo radius vectors and feature points Introduction (2) Why Face Recognition? Introduction (3) F 日 FR analyzes facial 口Non- intrusive More nature, do not restrict user movement- socially more acceptable aIt requir This is how human beings are recognizing each other LEss expensive to setup considerable interest. Hardware is getting cheaper ole many legacy uses/database of face images to construct ne lH-image with or without consent of the 日 Fight terrorism °9ada90% asing need after the september 11 events/ Spot terrorists in to 50% only !) Require automated face detection system on suspect in sensitive Face Recognition Vendor Tests(FRVT) areas,e.g.airport, military facility ●httpl/www.frvt.org1 Biometrics Research Centre (UGC/CRC) Lecture 6 - 1 Traditional Traditional Uni-Modal Face Recognition Face Recognition Biometric Security Biometric Security Lecture 8 Lecture 8 Biometrics Research Centre (UGC/CRC) Lecture 8 - 2 OUTLINE OUTLINE • Introduction • Face Recognition System • Face Detection & Location • Face Normalization • Feature Extraction & Recognition • Face Recognition Application • Face Recognition Problems Biometrics Research Centre (UGC/CRC) Lecture 8 - 3 Introduction Biometrics Research Centre (UGC/CRC) Lecture 8 - 4 ‰ Current State É Face is the most common biometrics. Using the whole face for automatic identification is a complex task because its appearance is constantly changing. É One effective approach may employ rule-based logic and a neural network for the image classification process. The first face system is introduced in 1992. ‰ Feature Set Facial geometry - Size of eye, distance from eye to mouth, middle of mouth to chin, side of eye to cheek, size of mouth, radius vectors and feature points Face Introduction (1) Introduction (1) Biometrics Research Centre (UGC/CRC) Lecture 8 - 5 Introduction (2) Introduction (2) Why Face Recognition? Why Face Recognition? ‰Non-intrusive z More nature, do not restrict user movement - Socially more acceptable z This is how human beings are recognizing each other ‰Less expensive to setup z Hardware is getting cheaper z Available many legacy uses/database of face images z Easy to construct new facial-image with or without consent of the people ‰Fight terrorism z Increasing need after the September 11 events/ Spot terrorists in public z Require automated face detection system on suspect in sensitive areas, e.g. airport, military facility Biometrics Research Centre (UGC/CRC) Lecture 8 - 6 ‰FR analyzes facial characteristics. ‰It requires a digital (web) camera (of low quality is enough). ‰This technique has attracted considerable interest. ‰Uses distinctive features of the human face to verify or identify individuals Introduction (3) Introduction (3) ‰ Accuracy: the best performance had a 90% verification rate at a FAR of 1%. (However, when the face is captured at outdoor, for the same 1% FAR, the verification rate is dropped to 50% only!) Face Recognition Vendor Tests (FRVT) zhttp://www.frvt.org/