Learning to represent 2D human face with mathematical model
Abstract How to represent a human face pattern? While it is presented in a continuous way in human visual system, computers often store and process it in a discrete manner with 2D arrays of pixels. The authors attempt to learn a continuous surface representation for face image with explicit function...
主要な著者: | Liping Zhang, Weijun Li, Linjun Sun, Lina Yu, Xin Ning, Xiaoli Dong |
---|---|
フォーマット: | 論文 |
言語: | English |
出版事項: |
Wiley
2024-02-01
|
シリーズ: | CAAI Transactions on Intelligence Technology |
主題: | |
オンライン・アクセス: | https://doi.org/10.1049/cit2.12284 |
類似資料
-
A Review of Face Recognition Technology
著者:: Lixiang Li, 等
出版事項: (2020-01-01) -
Understanding of facial features in face perception: insights from deep convolutional neural networks
著者:: Qianqian Zhang, 等
出版事項: (2024-04-01) -
MATHEMATICS LEARNING PROCESS AND RESULTS OF ELEMENTARY SCHOOL STUDENTS IN LIMITED FACE-TO-FACE LEARNING
著者:: Ariani Haffidah Lestari, 等
出版事項: (2023-04-01) -
A Systematic Review of CNN Architectures, Databases, Performance Metrics, and Applications in Face Recognition
著者:: Andisani Nemavhola, 等
出版事項: (2025-02-01) -
SeqFace: Learning discriminative features by using face sequences
著者:: Wei Hu, 等
出版事項: (2021-09-01)