Probabilistic DBNN via expectation-maximization with multi-sensor classification applications

The original learning rule of the decision based neural network (DBNN) is very much decision-boundary driven. When pattern classes are clearly separated, such learning usually provides very fast and yet satisfactory learning performance. Application examples including OCR and (finite) face/object recognition. Different tactics are needed when dealing with overlapping distribution and/or issues on false acceptance/rejection, which arises in applications such as face recognition and verification. For this, a probabilistic DBNN would be more appealing. This paper investigates several training rules augmenting probabilistic DBNN learning, based largely on the expectation maximization (EM) algorithm. The objective is to establish evidence that the probabilistic DBNN offers an effective tool for multi-sensor classification. Two approaches to multi-sensor classification are proposed and the (enhanced) performance studied. The first involves a hierarchical classification, where sensor information are cascaded in sequential processing stages. The second is multi-sensor fusion, where sensor information are laterally combined to yield improved classification. For the experimental studies, a hierarchical DBNN-based face recognition system is described. For a 38-person face database, the hierarchical classification significantly reduces the false acceptance (from 9.35% to 0%) and false rejection (from 7.29% to 2.25%), as compared to non-hierarchical face recognition. Another promising multiple-sensor classifier fusing face and palm biometric features is also proposed.