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This paper presents a gender classification schema based on onlinehandwriting. Using samples acquired with a digital tablet that captures thedynamics of the writing, it classifies the writer as a male or a female. Themethod proposed is allographic, regarding strokes as the structural units ofhandwriting. Strokes performed while the writing device is not exerting anypressure on the writing surface, pen-up (in-air) strokes, are also taken intoaccount. The method is also text-dependent meaning that training and testing isdone with exactly the same text. Text-dependency allows classification beperformed with very small amounts of text. Experimentation, performed withsamples from the BiosecurID database, yields results that fall in the range ofthe classification averages expected from human judges. With only fourrepetitions of a single uppercase word, the average rate of well classifiedwriters is 68%; with sixteen words, the rate rises to an average 72.6%.Statistical analysis reveals that the aforementioned rates are highlysignificant. In order to explore the classification potential of the pen-upstrokes, these are also considered. Although in this case results are notconclusive, an outstanding average of 74% of well classified writers isobtained when information from pen-up strokes is combined with information frompen-down ones.

Gender classification by means of online uppercase handwriting: A text-dependent allographic approach