Abstract
Recognizing gender of a person from occluded face image is a recent challenge in gender classification research. This work investigates the issue and proposes a gender classification system that works for non-occluded face images to face images occluded up to 60%. Local information of the face, which carries the most discriminative features to find the gender, is gathered by dividing the face image into M×N sub-images. Subsequently, features are calculated for every sub-image by applying (2D)2PCA on each illumination invariant real Gabor space generated using Gabor filter. Support Vector Machine is used for classification. Experiments are performed on five databases. In case of non-occluded face images, the proposed approach gives 98.4% classification rate on FERET database. For occluded face images, occlusions ranging from 10% to 60%, results are quite competitive with accuracies around 90%. Present work also analyzes the impact of various face components in the context of gender classification.
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