ing at the problem of low recognition rate because the single descriptor cannot accurately obtain the effective palmprint features, a palmprint recognition method is proposed based on subspace and texture feature fusion. The subspace feature and texture feature of a palmprint image are obtained by robust linear discriminant analysis and local direction binary pattern, respectively. The weighted concatenation method is used for the subspace and texture feature fusion. The chi-square distance among the fused feature vectors is used for identification matching. The experimental results on the PolyU and the self-built non-contact databases show that the recognition time is 0.3069 s and 0.3127 s, respectively, and the lowest equal error rate is only 0.3440% and 1.4922%, respectively. Compared with other methods, the proposed method can accurately obtain the effective feature information of a palmprint image and improve the system recognition performance under the premise that the real-time performance is ensured.

ing at the problem of serious lack of effective samples in the automatic discovery of camouflage targets, a simulation training method is proposed based on the sample simulation of a deep neural network and the technical idea of AlphaGo. A simulation synthesis model of camouflage scenes is established. The compound algorithm in the image space, the deep feature extraction strategy of scene images, the measurement strategy of target fusion degree, and the sampling algorithm for graph clustering are designed, respectively. Thus the representative samples for camouflage scene simulation are batch generated, which can be used for the deep neural network training and learning. Moreover, a discovery model of camouflage targets is designed based on a deep residual neural network, in which a multi-scale network training strategy is considered. The experimental results on the simulated samples and real scene images show that the proposed method can be effectively used for the automatic discovery and evaluation of camouflage targets.