ing at the spoofing attacks for the current face authentication systems, the traditional spoofing attacks include displaying printed photos and replaying recorded videos. With the rapid development of three-dimensional (3D) printing technology, the 3D mask spoofing attack is becoming a new threat. On the basis of the shearlet transform and combining with the 3D geometric attributes and the local regional texture changes, a method by utilizing the multilayer autoencoder network to conduct the feature fusion-based classification to identify the attack mask is proposed for the 3D mask spoofing attack. The low-frequency sub-band and several high-frequency sub-bands are extracted from the 3D image of the target face by the non sub-sampled shearlet transform method. The scale space function is used to detect, locate and distribute the feature points and then to generate feature operators in the low-frequency sub-band . Then, the generated feature operators and the texture features extracted from the high-frequency sub-band are combined in series and fed into the stacked autoencoder network and the softmax classifier to conduct the bottleneck feature fusion-based classification. The experimental results in the BFFD database based on the flexible TPU material 3D print mask shows that, the multi-feature fusion method added the 3D geometric feature has an obvious improvement for the accuracy of the anti-spoofing performance against 3D mask attacks to compare with the previous method of using the texture feature alone.

ed at the disadvantages of the lower azimuth resolution of the sky-wave radar and larger position error of traditional analytic algorithm, a new locating model using chaotic mutation grey wolf optimization algorithm to optimize the kernel extreme learning machine (KELM) is put forward. First, the piecewise linear chaotic map, adaptive Cauchy mutation strategy and non-linearity of the convergence factor are introduced into the grey wolf optimization algorithm to form an improved grey wolf algorithm. Then, the improved grey wolf optimization algorithm is used to optimize penalty coefficient and kernel parameter of the KELM. Finally, the optimized the KELM is applied to sky-wave radar location, making the established KELM model have the high steady-state prediction accuracy and generalization performance. The experimental results show that the predicted results of the proposed model are basically consistent with the measured values, and the prediction accuracy is higher than that of the KELM location model, which is optimized by the standard grey wolf algorithm. A new target location method is provided for sky-wave radar.