Modular neural network integrator for human recognition from ear images

We propose a modular neural network architecture in order to make easy and fast the recognition process of the ear as a biometric. Comparing with other biometrics, ear recognition has one of the best performances, even when it has not received much attention. To improve the performance for ear recognition and make a comparison with other existing methods, we used the 2D wavelet analysis with Global Thresholding method, and Sugeno Measure and Winner-Takes-All (WTA) as modular neural network integrator. Recognition results achieved was up to 97%.