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

As a framework of optical machine learning, all-optical diffractive neural network (DNN) 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 DNN 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 DNN. 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.