Neural networks; Face recognition; Ring–wedge detector
Use of neural networks (NNs) and diffraction pattern sampling by a ring–wedge detector leads to easier and faster algorithms for pattern recognition. An estimation was made of the optimum dimensions of a digital ring–wedge detector for sampling Fourier transform of random matrices through simulation of digital ring–wedge detector. The modulus squared Fourier transforms of facial images were sampled by ring–wedge geometry, and used for training a neural net for multi-face recognition. Fourier spectral intensities obtained by simulation and experiment were both tested for training and generalization of the network which was studied as a function of learning rate and number of epochs.