11 Facerecog

11 Facerecog - An Overview of Face Recognition Using...

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Eigenfaces 1 An Overview of Face Recognition Using Eigenfaces Acknowledgements: Original Slides from Prof. Matthew Turk -- also notes from the web -Eigenvalues and Eigenvectors -PCA -Eigenfaces Monday, February 22, 2010
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Outline Why automated face recognition? Eigenfaces and appearance-based approaches to recognition – Motivation – Review Why Eigenfaces? Why not Eigenfaces? Where shall we go from here? Monday, February 22, 2010
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Why Automated Face Recognition? It is a very vital and compelling human ability – Faces are important to us – Severe social problem for people who lack this ability It’s fun to work on – Better than recognizing tanks and sprockets Good, paradigmatic vision problem It may actually be useful – Biometrics, HCI, surveillance, … They can do it in the movies! Monday, February 22, 2010
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Commercial Interest Image and video indexing Biometrics, e-commerce – Visionics, Viisage, eTrue, … Surveillance – Casinos, Super Bowl, Tampa, FL Monday, February 22, 2010
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Automated Face Recognition Typical formulations: – Given an image of a face, who is it? (recognition) – Is this an image of Joe Schmoe? (verification) Why isn’t this easy? Monday, February 22, 2010
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The Problem The human face is an extremely complex object, highly deformable, with both rigid and non-rigid components that vary over time, sometimes quite rapidly and sometimes quite slowly The “object” is covered with skin, a non- uniformly textured material that is difficult to model either geometrically or photometrically Monday, February 22, 2010
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The Problem Time-varying changes include: – The growth and removal of facial hair, wrinkles and sagging of the skin brought about by aging, skin blemishes, changes in skin color and texture caused by exposure to sun, etc. Plus many common artifact-related changes: – Glasses, makeup, jewelry, piercings, cuts and scrapes, bandages, etc. Not to mention facial expressions, changes in hairstyle, etc. Monday, February 22, 2010
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The Problem In general, object recognition is difficult because of the immense variability of object appearance Several factors are all confounded in the image data – Shape, reflectance, pose, occlusion, illumination Human faces add more factors – Expression, facial hair, jewelry, etc. So… one may argue that face recognition is harder than most object recognition tasks Monday, February 22, 2010
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Overcoming these difficulties will be a significant step forward for the computer vision community So, face recognition has been considered a challenging problem in computer vision for some time now The amount of effort in the research community devoted to this topic has increased significantly over the years. Real-time performance is key!
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This note was uploaded on 12/29/2011 for the course ECE 181b taught by Professor Staff during the Fall '08 term at UCSB.

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11 Facerecog - An Overview of Face Recognition Using...

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