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How do you construct Eigenspace? W Construct data matrix by stacking vectorized images and then apply Singular Value Decomposition (SVD)Įigenfaces ModelingGiven a collection of n labeled training images,Compute mean image and covariance matrix.Compute k Eigenvectors (note that these are images) of covariance matrix corresponding to k largest Eigenvalues.Project the training images to the k-dimensional Eigenspace.RecognitionGiven a test image, project to Eigenspace.Perform classification to the projected training images.Įigenfaces: Training Images [ Turk, Pentland 01ĭifficulties with PCA Projection may suppress important detailsmallest variance directions may not be unimportantMethod does not take discriminative task into accounttypically, we wish to compute features that allow good discriminationnot the same as largest varianceįisherfaces: Class specific linear projection An n-pixel image xRn can be projected to a low-dimensional feature space yRm byy = Wxwhere W is an n by m matrix. L1, robust distances)Multiple templates per class- perhaps many training images per class.Expensive to compute k distances, especially when each image is big (N dimensional).May not generalize well to unseen examples of class.Some solutions:Bayesian classificationDimensionality reductionĮigenfaces (Turk, Pentland, 91) -1 Use Principle Component Analysis (PCA) to reduce the dimsionality Nearest Neighbor Classifier are set of training images.Ĭomments Sometimes called “Template Matching”Variations on distance function (e.g. Image as a Feature Vector Consider an n-pixel image to be a point in an n-dimensional space, x Rn.Each pixel value is a coordinate of x. Pose-dependent Algorithms Pose-invariant Pose-dependency Matching features Appearance-based (Holistic) - Gordon et al., 1995 Feature-based (Analytic) Hybrid Viewer-centered Images - Lengagne et al., 1996 - Atick et al., 1996 Object-centered Models - Yan et al., 1996 - Zhao et al., 2000 Face representation - Zhang et al., 2000 PCA, LDA LFA EGBM Taxonomy of Face Recognition
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Computer Vision and Pattern Recognition, 1999 copyright 1999, IEEEįace Detection Algorithm Face Localization Lighting Compensation Skin Color Detection Color Space Transformation Variance-based Segmentation Connected Component &Grouping Face Boundary Detection Verifying/ WeightingEyes-Mouth Triangles Eye/ Mouth Detection Facial Feature Detection Input Image Output Imageįace Recognition: 2-D and 3-D 2-DFace Database 2-DRecognitionData RecognitionComparison Prior knowledgeof face class Sketch of a Pattern Recognition Architecture FeatureExtraction Classification Image(window) ObjectIdentity Feature VectorĮxample: Face Detection Scan window over imageClassify window as either:FaceNon-faceįace Detection: Experimental Results Test sets: two CMU benchmark data setsTest set 1: 125 images with 483 facesTest set 2: 20 images with 136 faces Įxample: Finding skin Non-parametric Representation of CCD Skin has a very small range of (intensity independent) colors, and little textureCompute an intensity-independent color measure, check if color is in this range, check if there is little texture (median filter)See this as a classifier - we can set up the tests by hand, or learn them.get class conditional densities (histograms), priors from data (counting)Classifier isįigure from “Statistical color models with application to skin detection,” M.J. Intra-class Variability Faces with intra-subject variations in pose, illumination, expression, accessories, color, occlusions, and brightness Inter-class Similarity Different persons may have very similar appearance Twins Father and son .uk/hi/english/in_depth/americas/2000/us_elections Why is Face Recognition Hard? Many faces of Madonnaįace Recognition Difficulties Identify similar faces (inter-class similarity)Accommodate intra-class variability due to:head poseillumination conditionsexpressionsfacial accessoriesaging effects Cartoon faces Video Surveillance (On-line or off-line) Applications Face Scan at Airports Slide 7.
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Face Recognition Face is the most common biometric used by humansApplications range from static, mug-shot verification to a dynamic, uncontrolled face identification in a cluttered backgroundChallenges: automatically locate the face recognize the face from a general view point under different illumination conditions, facial expressions, and aging effectsĪuthentication vs Identification Face Authentication/Verification (1:1 matching)Face Identification/Recognition (1:N matching)