Title :
Text-Independent Writer Identification and Verification Using Textural and Allographic Features
Author :
Bulacu, Marius ; Schomaker, Lambert
Author_Institution :
AI Inst., Groningen Univ.
fDate :
4/1/2007 12:00:00 AM
Abstract :
The identification of a person on the basis of scanned images of handwriting is a useful biometric modality with application in forensic and historic document analysis and constitutes an exemplary study area within the research field of behavioral biometrics. We developed new and very effective techniques for automatic writer identification and verification that use probability distribution functions (PDFs) extracted from the handwriting images to characterize writer individuality. A defining property of our methods is that they are designed to be independent of the textual content of the handwritten samples. Our methods operate at two levels of analysis: the texture level and the character-shape (allograph) level. At the texture level, we use contour-based joint directional PDFs that encode orientation and curvature information to give an intimate characterization of individual handwriting style. In our analysis at the allograph level, the writer is considered to be characterized by a stochastic pattern generator of ink-trace fragments, or graphemes. The PDF of these simple shapes in a given handwriting sample is characteristic for the writer and is computed using a common shape codebook obtained by grapheme clustering. Combining multiple features (directional, grapheme, and run-length PDFs) yields increased writer identification and verification performance. The proposed methods are applicable to free-style handwriting (both cursive and isolated) and have practical feasibility, under the assumption that a few text lines of handwritten material are available in order to obtain reliable probability estimates
Keywords :
handwriting recognition; pattern clustering; statistical distributions; text analysis; allographic features; automatic writer identification; behavioral biometrics; biometric modality; character-shape analysis; common shape codebook; contour-based joint directional PDF; curvature information; forensic document analysis; free-style handwriting; grapheme clustering; handwriting images; historic document analysis; ink-trace fragments; person identification; probability distribution functions; stochastic pattern generator; text-independent writer identification; textural features; texture level analysis; Biometrics; Character generation; Forensics; Image analysis; Image texture analysis; Pattern analysis; Probability distribution; Shape; Stochastic processes; Text analysis; Handwriting analysis; behavioral biometrics; grapheme-emission probability distribution.; joint directional probability distributions; writer identification and verification; Algorithms; Artificial Intelligence; Automatic Data Processing; Biometry; Handwriting; Image Enhancement; Image Interpretation, Computer-Assisted; Numerical Analysis, Computer-Assisted; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Subtraction Technique; User-Computer Interface;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.2007.1009