Abstract
An enhanced algorithm to recognize the human face using bi-dimensional fractal codes and deep belief networks is presented in this work. The proposed method is experimentally robust against variations in the appearance of human face images, despite different disturbances affecting the measurements and the acquisition process such as occlusion, changes in lighting, pose, and expression or the presence or absence of structural components. That is mainly based on fractal codes (IFS) and bi-dimensional subspaces for features extraction and space reduction, combined with a deep belief network (DBN) classifier. The evaluation is performed through comparisons using probabilistic neural network (PNN) and nearest neighbours (KNN) approaches on three well-known databases (FERET, ORL, and FEI). The results suggest the effectiveness and robustness of the proposed approach.
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