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Facial Kinship Verification [electronic resource] : A Machine Learning Approach / by Haibin Yan, Jiwen Lu.

By: Contributor(s): Material type: TextSeries: Publisher: Singapore : Springer Nature Singapore : Imprint: Springer, 2017Edition: 1st ed. 2017Description: X, 82 p. 33 illus., 29 illus. in color. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9789811044847
Subject(s): DDC classification:
  • 006.37 23
Online resources:
Contents:
1. Introduction to Facial Kinship Verification -- 2. Feature Learning for Facial Kinship Verification -- 3. Metric Learning for Facial Kinship Verification -- 4. Video-Based Facial Kinship Verification -- 5. Conclusions and Future Work.
Summary: 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.
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1. Introduction to Facial Kinship Verification -- 2. Feature Learning for Facial Kinship Verification -- 3. Metric Learning for Facial Kinship Verification -- 4. Video-Based Facial Kinship Verification -- 5. Conclusions and Future Work.

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.

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