Extraction of Feeling Information From Characters Using a Modified Fourier Transform.
Available from: Marcus Liwicki
[Show abstract] [Hide abstract]
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 off-line
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.
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.