ID Image Characterization by Entropic Biometric Decomposition

The paper proposes a statistical-based biometric decomposition for ID image recognition robust to a series of non malicious attacks generated by print/scan operations. Our goal is to label the single face expression by a signature, which is almost invariant to low filtering, noise addition and geometric attacks. The method is based on Independent Component Analysis (ICA) in a configuration which will allow a decomposition into some face characteristics. In this configuration known in literature as Architecture I, the most important coefficients issued from ICA are selected by looking for the independent components with maximum local entropy. A biometric label of fixed length is associated to any ID image to be enrolled, after projection on the learned basis, uniform quantization of the obtained coefficients and binary encoding. Two parameters were tuned: the number of quantization levels and the number of face characteristics. The latter one was modified, either by discarding coefficients after Principal Component Analysis in the beginning of FastICA algorithm, or by selecting the most prominent biometric features by applying an entropic criterion. The suggested method inherits the robustness of a global approach.