Signature Verification

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Dimension Reduction• PCA– Transform a 3D vector of highly correlated data to 1D– Find a linear transformation W that maps the originalvector (X) to projection coefficient vector (Y)Y=WX– Compute Average:– Covariance Matrix:T– Eigen-decomposition:SµT==S TN∑1N∑N i = 1i = 1x i( x − µ )( x − µ )e = λeiiT
PCA• Project the distance vector (max, min,template) to 1 dimension
Page 5: • On-Line:Azimuth (0°-359°)Alti
Page 8: • Disadvantages:- High intra-clas
Page 11: • Acquisition devices:AlAzPYX0 10
Page 14 and 15: • Position Normalization:10008006
Page 16 and 17: Resampling• Ensures that the sign
Page 18 and 19: Stroke Concatenation• A stroke is
Page 20 and 21: Critical Points• Re-sampling or s
Page 22 and 23: Preprocessing Steps• Fine re-samp
Page 24 and 25: Pattern Recognition Process
Page 26 and 27: Feature Extraction• Local feature
Page 28 and 29: Local Features• The 9x9 pixel nei
Page 30 and 31: Global Features
Page 32 and 33: String Matching• To compute this
Page 34 and 35: String Matching• Each point is re
Page 38 and 39: Validation Set• PCA needs samples
Page 40 and 41: • M is the squared Mahalanobis di
Page 42 and 43: Score Fusion
Page 44 and 45: Weighted Sum Rule• Assign a test

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