Titlebook: Biometric Recognition; 15th Chinese Confere Jianjiang Feng,Junping Zhang,Yuchun Fang Conference proceedings 2021 Springer Nature Switzerlan

Pituitary-Gland

Skeleton-Based Action Recognition with Improved Graph Convolution Networkters, we propose a evaluation criterion with less computational effort. We perform extensive experiments on the Kinetics dataset and the NTU RGB+D dataset to verify the effectiveness of each network of our model. The comparison results show that our approach achieves satisfactory results.

Adjourn

Mouse Dynamics Based Bot Detection Using Sequence Learningver the raw mouse movement sequence into suitable input formats for deep learning models. Experimental results demonstrate that our method outperforms existing machine learning methods with handcrafted features and the deep learning method with visualization representation with a detection accuracy of 99.78% for the bot.

高度

噱頭

rectocele

DEFT

Defense

3D Context-Aware PIFu for Clothed Human Reconstructioneconstruction network to capture fine-grained geometry on 3D cloth, we propose a multi-view implicit differentiable loss to directly measure the visual effect. Experimental results show that our approach is more robust to pose variations and reconstructs the human body with more details.

consolidate

Facial Expression Synthesis with Synchronous Editing of Face Organskey regions. From the point of frequency domain, it is probably caused by the distortion of high frequency information. After this we add a spectrum restriction loss to original training losses in order to improve the fidelity of generated faces. Extensive experiments prove our model a great success on two widely-used datasets: MUG and CASIA-Oulu.

Pamphlet

End-To-End Finger Trimodal Features Fusion and Recognition Model Based on CNNr can get high recognition accuracy 99.83%. It shows that the fusion feature obtained by the proposed model possessing good individual characterization ability can effectively improve recognition accuracy.

MAL

Traumatic Brain Injury Images Classification Method Based on Deep Learninglocal optima. The method has been verified on 636 brain CT images, and achieved classification accuracy of 89.3%. Additionally, the effectiveness of SE module and PCR module is verified through the ablation experiments. In comparison with other state-of-the-art methods, our method has better perform