An improved all-optical diffractive deep neural network with less parameters for gesture recognition

Abstract

As a framework of optical machine learning, all-optical diffractive neural network (D${}^2$NN) has delivered an ideal outcome of feature detection and target classification, currently raising high interest in the optics and photonics community. In this paper, we applied an improved D${}^2$NN architecture to the field of gesture recognition, which features more complicated contour than the common MNIST handwriting recognition in the previous literature. The proposed network structure incorporates the wavelet-like phase modulation pattern technique and the highway network on the basis of all-optical neural network. Through modulating the phase of incident light, the wavelet-like pattern can substantially reduce the parameters in the network layer. In addition, a highway network is employed to address the vanishing gradient phenomenon in the training process. In the experiment, we numerically achieved blind testing accuracy of 95.6% for identifying ten different gestures, and the number of parameters is only 3% of the regular D${}^2$NN. Reliability test and analysis show that the proposed method is a high-efficiency solution with low-parameters expecting for implementation of various machine learning tasks.