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Erschienen in:
2015 | OriginalPaper | Buchkapitel
Gender Classification from Face Images Based on Gradient Directional Pattern (GDP)
verfasst von : Faisal Ahmed, Padma Polash Paul, Patrick Wang, Marina Gavrilova
Erschienen in: Computational Science and Its Applications -- ICCSA 2015
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This paper presents an appearance-based facial feature descriptor based on the gradient directional pattern (GDP) for gender classification from face images. The GDP operator encodes the texture information of a local neighborhood by quantizing the gradient directions of the neighbors with respect to the center. The facial feature descriptor is computed by first dividing the face image into a number of sub-regions and then concatenating the individual GDP histograms computed from the corresponding sub-regions. Then, principal component analysis (PCA) is applied on the obtained face descriptor in order to reduce the feature dimensionality. We use a support vector machine (SVM) for the classification task. Experimental analysis on a large database comprising 1800 facial images shows promising results for the proposed method, as compared to some well-known appearance-based face descriptors.