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Owing to the complexity of finger vein patterns in shape and spatial dependence, the existing methods suffer from an inability to obtain accurate and stable finger vein features. This paper, so as to compensate this defect, proposes an end-to-end model to extract vein textures through integrating the fully convolutional neural network (FCN) with conditional random field (CRF). Firstly, to reduce missing pixels during ROI extraction, the method of sliding window summation is employed to filter and adjusted with self-built tools. In addition, the traditional baselines are endowed with different weights to automatically assign labels. Secondly, the deformable convolution network, through replacing the plain counterparts in the standard U-Net mode, can capture the complex venous structural features by adaptively adjusting the receptive fields according to veins' scales and shapes. Moreover, the above features can be further mined and accumulated by combining the recurrent neural network (RNN) and the residual network (ResNet). With the steps mentioned above, the fully convolutional neural network is constructed. Finally, the CRF with Gaussian pairwise potential conducts mean-field approximate inference as the RNN, and then is embedded as a part of the FCN, so that the model can fully integrate CRF with FCNs, which provides the possibility to involve the usual back-propagation algorithm in training the whole deep network end-to-end. The proposed models in this paper were tested on three public finger vein datasets SDUMLA, MMCBNU and HKPU with experimental results to certify their superior performance on finger-vein verification tasks compared with other equivalent models including U-Net.

Facial beauty prediction (FBP), as a frontier topic in the domain of artificial intelligence regarding anthropology, has witnessed some good results as deep learning technology progressively develops. However, it is still limited by the complexity of the deep structure network in need of a large number of parameters and high dimensions, easily leading to a great consumption of time. To solve this problem, this paper proposes a fast training FBP method based on local feature fusion and broad learning system (BLS). Firstly, two-dimensional principal component analysis (2DPCA) is employed to reduce the dimension of the local texture image so as to lessen its redundancy. Secondly, local feature fusion method is adopted to extract more advanced features through avoiding the effects from unstable illumination, individual differences, and various postures. Finally, extensional feature eigenvectors are input to the broad learning network to train an efficient FBP model, which effectively shortens operational time and improve its preciseness. Extensive experiments with the proposed method on large scale Asian female beauty database (LSAFBD) can be conducted within 13.33s while sustaining an accuracy of 58.97%, impressively outstripping other state-of-the-art methods in training speed. INDEX TERMS Facial beauty prediction (FBP); local feature fusion; broad learning system (BLS);

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Background For the encoding part of U-Net3+,the ability of brain tumor feature extraction is insufficient, as a result, the features can not be fused well during up-sampling, and the accuracy of segmentation will reduce. Methods In this study, we put forward an improved U-Net3+ segmentation network based on stage residual. In the encoder part, the encoder based on the stage residual structure is used to solve the vanishing gradient problem caused by the increasing in network depth, and enhances the feature extraction ability of the encoder which is instrumental in full feature fusion when up-sampling in the network. What’s more, we replaced batch normalization (BN) layer with filter response normalization (FRN) layer to eliminate batch size impact on the network. Based on the improved U-Net3+ two-dimensional (2D) model with stage residual, IResUnet3+ three-dimensional (3D) model is constructed. We propose appropriate methods to deal with 3D data, which achieve accurate segmentation of the 3D network. Results The experimental results showed that: the sensitivity of WT, TC, and ET increased by 1.34%, 4.6%, and 8.44%, respectively. And the Dice coefficients of ET and WT were further increased by 3.43% and 1.03%, respectively. To facilitate further research, source code can be found at: https://github.com/YuOnlyLookOne/IResUnet3Plus. Conclusion The improved network has a significant improvement in the segmentation task of the brain tumor BraTS2018 dataset, compared with the classical networks u-net, v-net, resunet and u-net3+, the proposed network has smaller parameters and significantly improved accuracy.