Multi-layer networks are perhaps the simplest and most popular supervised learning model and can be adopted for most biometric authentication applications. Structurally, a multi-layer network has full connectivity, as illustrated in Figure 6.1(a). More precisely, all hidden nodes of one lower layer are fully connected to all nodes in its immediate subsequent layer. In other words, the model adopts a flat network structure such that all synaptic weights of a layer are lumped together in one supernetwork. This type of network is also termed "all-class-one-network" (ACON) .