FACE RECOGNITION AUTHOR: Łukasz Przywarty - 171018

Table of contents Introduction Recognition process Face detection Feature extraction Face recognition Application example Summary Literature Face recognition – 2/18

Introduction Why? Face recognition – 3/18 Areas Applications Information Security Access security (OS, data bases) Data privacy (e.g. medical records) User authentication (trading, on line banking) Access management Secure access authentication (restricted facilities) Permission based systems Access log or audit trails Biometrics Person identification (national IDs, Passports, voter registrations, driver licenses) Automated identity verification (border controls) Law Enforcement Video surveillance Suspect identification Suspect tracking (investigation) Simulated aging Forensic Reconstruction of faces from remains Personal security Home video surveillance systems Expression interpretation (driver monitoring system) Entertainment - Leisure Home video game systems Photo camera applications Face recognition – 3/18

Introduction Since when? 1960’s – semi-automated system: required the administrator to locate face coordinates; computer used this for recognition 1970’s – Goldstein, Harmon, Lesk: vector containing 21 features e.g eyebrow weight, nose length as the basis to recognize faces (pattern classification) 1986 – Kirby, Sirovich: methods based on PCA (Principal Component Analysis); goal: represent image in lower dimension without losing much information; dominant approach in following years Face recognition – 4/18

Introduction Problems? Pose variations Observation conditions (angle, light, shadows, reflections etc.) Ageing Facial expression Facial occulsion: make-up, hair style, accesories Face recognition – 5/18

Identification or verification Recognition process How to do it? How to detect face? Detection depending on scenario: Controlled environment – simple edge detection techniques Color images – skin colors can be used to find faces Images in motion – e.g blink detection Input Face detection Feature extraction Face recognition Identification or verification Face recognition – 6/18

Recognition process How to detect face? Detection methods: Knowledge –based methods : they try to capture our knowledge of faces and translate them into set of rules (face has two symmetric eyes, the eye area is darker than the cheeks etc), facial features could be the distance between eyes or color intensity difference. Feature-invariant methods: algorithms that try to find invariant features of a face despite it’s angle or position Face recognition – 7/18

Recognition process How to detect face? for example: algorithms that detect face-like textures or the color of human skin. Template matching try to define face as a function and find a standard template of all the faces, template colud be: face contour, relation between face regions in terms of brightness and darkness, limited to faces that are frontal. Appearance-based methods statistical analysis. Face recognition – 8/18

Recognition process How to standarize image? Histogram modification Image filtration Geometrical transformation Rotate Scale Move Resize Desaturation or color modification Face recognition – 9/18

Division of face recognition systems Feature-based approach First, most intuitive idea First step: localization of points on face images: eyes centre points nose start-end points etc. Next step: measuring: face, nose width, height etc. distances between eyes centres, nose and eyes etc. Problems Accurate points localization Face recognition – 10/18

Division of face recognition systems Feature-based approach Used methods: Geometric Matching Bunch Graph Matching Hidden Markov Model Techniques Face recognition – 11/18

Division of face recognition systems Holistic approach Whole face analysis Methods based on: Correlation: simple method operating on input image pixels, direct comparision to a pattern in database, works if images were taken in almost the same conditions PCA (Principal Component Analysis ) and eigenfaces concept: feature dimension reduction (converts two dimensional vectors into one dimensional vector) extracts the features of face which vary the most, Face recognition – 12/18

Division of face recognition systems Holistic approach problem: image must be the same size and normalized; pose and illumination variation in not acceptable, rate od recognition: 95% LDA (Linear Discriminate Analysis) and Fisherface concept Face recognition – 13/18

Division of face recognition systems Hybrid approach Both local feature and whole face Methods based on: AAM (Active Appearance Model) integrated statistical model which combines a model of shape variation and apperance with new image, built during a training phase, compares both whole face shape and pixels brightness around feature. Face recognition – 14/18

Application example Picasa 3.5 Static images Luxand FaceSDK 66 feature points -30-30 degrees head rotation support 49 700 faces per second Verilook 5.1 Multiface processing Live face detection Tolerance to face posture (near 360 degrees) 44 000 faces per second Multiple samples of same face Face recognition – 15/18

Final word Summary? Despite of 40 years development still unreliable 12% of biometric technologies (2nd place, after print) Low effectiveness in pilot projects (UK: Newham, USA: Tampa) Failed trial in airports Face recognition – 16/18

Literature E. Bagherian, R. Wirza O.K. Rahmat. „Facial feature extraction for face recognition: a review” C. Iancu, P. Corcoran, G. Costache . „A review of face recognition techniques for in-camera applications” M. Smiatacz, W. Malina. „Automatic face recognition – methods, problems and applications” K. Ślot. „Rozpoznawanie biometryczne” K. Ślot. „Wybrane zagadnienia biometrii” Face recognition – 17/18

FACE RECOGNITION Thank you for your attention!