  This paper presents a gender classification schema based on online
handwriting. Using samples acquired with a digital tablet that captures the
dynamics of the writing, it classifies the writer as a male or a female. The
method proposed is allographic, regarding strokes as the structural units of
handwriting. Strokes performed while the writing device is not exerting any
pressure on the writing surface, pen-up (in-air) strokes, are also taken into
account. The method is also text-dependent meaning that training and testing is
done with exactly the same text. Text-dependency allows classification be
performed with very small amounts of text. Experimentation, performed with
samples from the BiosecurID database, yields results that fall in the range of
the classification averages expected from human judges. With only four
repetitions of a single uppercase word, the average rate of well classified
writers is 68%; with sixteen words, the rate rises to an average 72.6%.
Statistical analysis reveals that the aforementioned rates are highly
significant. In order to explore the classification potential of the pen-up
strokes, these are also considered. Although in this case results are not
conclusive, an outstanding average of 74% of well classified writers is
obtained when information from pen-up strokes is combined with information from
pen-down ones.
