Zusammenfassung
We have investigated the problem of gender
classification using a library of four hundred standard
frontal facial images employing five classifiers,
namely k-means, k-nearest neighbours, linear
discriminant analysis (LDA), Mahalanobis distance based
(MDB) classifiers and our modified KNN classifier. The
image data independent discrete cosine transformation
(DCT) basis is used for facial feature extraction.
Areas under the convex hull (AUCH) of the classifiers
are measured by varying the values of threshold for
each feature subset in the receiver operating
characteristics (ROC) curve. The scalar values of AUCH
of the ROC curve increases with increasing number of
features. More features yield a better representation
of the gender facial image. The overall performance of
classifiers is compared with different values of AUCH
versus features under different conditions. It has been
observed that when the number of features is increased
beyond 5, AUCH starts to saturate. Our experimental
results demonstrate that modified-KNN performs better
than the rest of the conventional classifiers under all
conditions. The LDA classifier did not perform well in
the DCT domain; however, it gradually improved its
performance with increasing number of features.
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