3D face recognition algorithm based on deep Laplacian pyramid under the normalization of epidemic control

Abstract

Under the normalization of epidemic control in COVID-19, it is essential to realize fast and high-precision face recognition without feeling for epidemic prevention and control. This paper proposes an innovative Laplacian pyramid algorithm for deep 3D face recognition, which can be used in public. Through multi-mode fusion, dense 3D alignment and multi-scale residual fusion are ensured. Firstly, the 2D to 3D structure representation method is used to fully correlate the information of crucial points, and dense alignment modeling is carried out. Then, based on the 3D critical point model, a five-layer Laplacian depth network is constructed. High-precision recognition can be achieved by multi-scale and multi-modal mapping and reconstruction of 3D face depth images. Finally, in the training process, the multi-scale residual weight is embedded into the loss function to improve the network’s performance. In addition, to achieve high real-time performance, our network is designed in an end-to-end cascade. While ensuring the accuracy of identification, it guarantees personnel screening under the normalization of epidemic control. This ensures fast and high-precision face recognition and establishes a 3D face database. This method is adaptable and robust in harsh, low light, and noise environments. Moreover, it can complete face reconstruction and recognize various skin colors and postures.