Dimensionality reduction of face images for gender classification

Data in most real world applications are high dimensional and learning algorithms like neural networks have problems in handling high dimensional data. However, the intrinsic dimension is often much less than the original dimension of the data. We use a fractal based method to estimate the intrinsic dimension and show that a nonlinear projection method called curvilinear component analysis can effectively reduce the original dimension to the intrinsic dimension. We apply this approach for dimensionality reduction of the face images data and use neural network classifiers for gender classification.