@article{pub5225,
    abstract = {In this paper, the problem of classifying handwritten
data with respect to gender is addressed. A classification
method based on Gaussian Mixture Models is
applied to distinguish between male and female handwriting.
Two sets of features using on-line and off-line information
have been used for the classification. Furthermore,
we combined both feature sets and investigated several
combination strategies. In our experiments, the on-line
features produced a higher classification rate than the offline
features. However, the best results were obtained with
the combination. The final gender detection rate on the test
set is 67.57%, which is significantly higher than the performance
of the on-line and off-line system with about
64.25 and 55.39%, respectively. The combined system also
shows an improved performance over human-based classification.
To the best of the authors’ knowledge, the system
presented in this paper is the first completely automatic
gender detection system which works on on-line data.
Furthermore, the combination of on-line and off-line features
for gender detection is investigated for the first time
in the literature.},
    year = {2010},
    title = {Automatic Gender Detection using On-Line and Off-Line Information},
    journal = {Pattern Analysis and Applications (PAA)},
    volume = {1},
    pages = {1-6},
    publisher = {Springer, London},
    author = {Marcus Liwicki and Andreas Schlapbach and Horst Bunke},
    url = {http://www.springerlink.com/content/x746251125w56g8l/fulltext.pdf}
}