Optimization of decision making for face recognition based on nonlinear correlation plane

We report on a specific procedure in the correlation plane which allows us to make more robust and discriminating decision for face recognition applications. In this scheme, we multiply the correlation plane by a nonlinear function which is chosen to increase the correlation peak, reduce the autocorrelation noise, and increase the inter-correlation noise. In our work we present tests using a VanderLugt correlator (VLC) fabricated with different filters (phase-only filter (POF), composite POF) and we also discuss the efficiency of the protocol using peak-to-correlation-energy measures. Rewardingly, our technical results demonstrate that this method is remarkably efficient to increase both robustness and discrimination performances of a VLC.