ONYM
发表于 2025-3-25 07:18:32
https://doi.org/10.1007/978-3-319-69923-3biometrics; speech recognition; activity recognition and understanding; online handwriting recognition;
PALL
发表于 2025-3-25 10:26:25
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Confess
发表于 2025-3-25 11:46:57
Conference proceedings 2017ewed and selected from 138 submissions. The papers are organized in topical sections on face; . fingerprint, palm-print and vascular biometrics; iris; gesture and gait; emerging biometrics;. voice and speech; video surveillance; feature extraction and classification theory; behavioral. biometrics..
addict
发表于 2025-3-25 16:37:43
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藐视
发表于 2025-3-25 23:23:53
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Nefarious
发表于 2025-3-26 01:19:01
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Manifest
发表于 2025-3-26 05:55:20
Deep Embedding for Face Recognition in Public Video Surveillanceearning, while there is still large gap between academic research and practical application. This work aims to identify few suspects from the crowd in real time for public video surveillance, which is a large-scale open-set classification task. The task specific face dataset is built from security s
glacial
发表于 2025-3-26 10:35:45
Random Feature Discriminant for Linear Representation Based Robust Face Recognitioned as a linear combination of training samples. Then the classification decision is made by evaluating which class leads to the minimum class-wise representation error. However, these two steps have different goals. The representation step prefers accuracy while the decision step requires discrimina
确认
发表于 2025-3-26 13:18:00
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幻想
发表于 2025-3-26 17:37:30
Max-Feature-Map Based Light Convolutional Embedding Networks for Face Verificationion. However, this category of models tend to be deep and paralleled which is not capable to be applied in real-time face recognition tasks. In order to improve its feasibility, we propose a max-feature-map activation based fully convolutional structure to extract face features with higher speed and