Titlebook: Biometric Recognition; 17th Chinese Confere Wei Jia,Wenxiong Kang,Jun Wang Conference proceedings 2023 The Editor(s) (if applicable) and Th

考古學(xué)

Luc Steels,Joachim De Beule,Pieter Wellensttack performance. However, in practical applications, it is inevitably affected by certain external environments and bring out performance reduction, such as the droplet problem, which is rarely solved in current research works nevertheless. Facing this challenge, this paper proposes a feature-fuse

MULTI

An Experiment in Temporal Language Learning door locks and mobile phone security, to more critical realms like public security and legal identification. However, traditional contact-based fingerprint recognition methods bear the drawbacks of compromising the fingerprint’s intrinsic 3D structure and being susceptible to contamination. In cont

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Katrien Beuls,Remi van Trijp,Pieter Wellensunderlying issues such as uneven illumination, low contrast, and sparse patterns with high inter-class similarities make the traditional vein recognition systems based on hand-engineered features unreliable. To address the difficulty of direct training or fine-tuning a CNN with existing small-scale

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Fluid Construction Grammar on Real Robotstional machine learning or deep learning have been proposed. However, the generalization ability of these methods is not satisfying due to the different entities, backgrounds, and sensors. In this paper, based on the three modalities of fingerprint, fingervein, and palmprint, the texture prior knowl

MITE

Lecture Notes in Computer Sciencentity-related representation. However, most existing facial age estimation methods usually extract age features from the RGB images, making them sensitive to the gender, race, pose and illumination changes. In this paper, we propose an end-to-end multi-feature integrated network for robust RGB-D fac

diabetes

SpringerBriefs in Speech Technologypoint (ICP) require laborious pairwise registration, so in this paper we focus on deep learning methods for human identification using tooth. We propose a complete workflow for tooth segmentation and recognition based on PointNet++?using the 3D intraoral scanning (IOS) model. Our method consists of

expository

知識(shí)分子

https://doi.org/10.1007/978-3-319-17163-0atabase based on queries given as texts, which holds significant potential for practical applications in public security and multimedia. Our approach employs a vision-language pre-training model as the backbone, effectively incorporating contrastive learning, image-text matching learning, and masked

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