Facial Kinship Verification
A Machine Learning Approach
Nonfiction, Computers, Advanced Computing, Engineering, Computer Vision, Application Software, Computer Graphics, General Computing
| Author: | Haibin Yan, Jiwen Lu | ISBN: | 9789811044847 |
| Publisher: | Springer Singapore | Publication: | May 31, 2017 |
| Imprint: | Springer | Language: | English |
| Author: | Haibin Yan, Jiwen Lu |
| ISBN: | 9789811044847 |
| Publisher: | Springer Singapore |
| Publication: | May 31, 2017 |
| Imprint: | Springer |
| Language: | English |
This book provides the first systematic study of facial kinship verification, a new research topic in biometrics. It presents three key aspects of facial kinship verification: 1) feature learning for kinship verification, 2) metric learning for kinship verification, and 3) video-based kinship verification, and reviews state-of-the-art research findings on facial kinship verification.
Many of the feature-learning and metric-learning methods presented in this book can also be easily applied for other face analysis tasks, e.g., face recognition, facial expression recognition, facial age estimation and gender classification. Further, it is a valuable resource for researchers working on other computer vision and pattern recognition topics such as feature-learning-based and metric-learning-based visual analysis.
This book provides the first systematic study of facial kinship verification, a new research topic in biometrics. It presents three key aspects of facial kinship verification: 1) feature learning for kinship verification, 2) metric learning for kinship verification, and 3) video-based kinship verification, and reviews state-of-the-art research findings on facial kinship verification.
Many of the feature-learning and metric-learning methods presented in this book can also be easily applied for other face analysis tasks, e.g., face recognition, facial expression recognition, facial age estimation and gender classification. Further, it is a valuable resource for researchers working on other computer vision and pattern recognition topics such as feature-learning-based and metric-learning-based visual analysis.
