Novel Modular Weightless Neural Architectures for Biometrics-based Recognition

We introduce a novel weightless artificial neural architecture based on multiple classifier systems. In this, different modules of a network specialise in recognising specific classes of a multiclass recognition task. Each of these modules comprises individual RAM addresses which store frequency-based probabilistic estimates of how likely it is to observe this pattern as a feature of the training examples available from a particular class. The class-wise likelihood of observing a combination of addresses for each class is calculated as a sum-based scheme (one of the most commonly used multi-classifier fusion methods). The classification decision is finally obtained by choosing the class with the highest pseudo-posterior probability for an address combination. Tests of our system on a face recognition problem using Minchinton cell encoding for mapping regions of interest (ROIs) to the network’s input layer showed very encouraging results.