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

可卡

Research approaches and methods point, will provide an important foundation for the automatic localization of auricular point, favoring nonexperts to increase their understanding of auricular acupressure therapy or the development of an intelligent instrument related to auricular acupressure therapy.

Ankylo-

https://doi.org/10.1057/9781137316202s show that the data fusion method and the fusion model of multiple machine learning models proposed in this paper can effectively improve the prediction accuracy. Our model can identify AD patients and detect abnormal brain regions and risk SNPs associated with AD, which can perform the association of risk SNPs with abnormal brain regions.

軍火

aspect

Personal Identification with Exploiting Competitive Tasks in EEG Signals identification task and event recognition task are trained together in an adversarial way to extract event-independent feature. Experimental results validate the effectiveness of the proposed method.

壟斷

A Systematical Solution for Face De-identificationfferent from previous traditional adversarial methods. Through this, we can construct unrestricted adversarial image to decrease ID similarity recognized by model. Our method can flexibly de-identify the face data in various ways and the processed images have high image quality.

slipped-disk

細(xì)微的差異

Auricular Point Localization Oriented Region Segmentation for Human Ear point, will provide an important foundation for the automatic localization of auricular point, favoring nonexperts to increase their understanding of auricular acupressure therapy or the development of an intelligent instrument related to auricular acupressure therapy.

形狀

slow-wave-sleep

Multi-lingual Hybrid Handwritten Signature Recognition Based on Deep Residual Attention Networkk, and the high-dimensional features are extracted automatically by the fusion channel attention for classification. The experimental results show that the highest recognition accuracy of this method is 99.44% for multi-lingual hybrid handwritten signature database, which has a high application value.

碎石頭