Signature Verification

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String Matching• Each point is represented by an n-ary feature vector– Feature reduction is performed• Euclidean distance is used as the metric to compare two featurevectors• Each feature is normalized using the z-score–f′=f− µσ• A set of pairings between thetemplate and input string is foundwhere the sum of the differencesbetween each pair of aligned pointsin minimal
User Dependent Normalization• Training set:– Pair-wise distances between all training samples is calculated(DTW)– The sample with the smallest average distance is selected asthe template– Normalization Statistics:• Average distance from template• Average maximum distance• Average minimum distance• Test sample:– Compute DTW against all training samples• Record: distance from template, maximum distance and minimumdistance– Normalize the 3 distances by dividing them by the set’s averagestatistics
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 36 and 37: Dimension Reduction• PCA- Transfo
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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