Self-adaptive morphable model based collaborative multi-view 3d face reconstruction in visual sensor network

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• 作者：Kuicheng Lin ; Xue Wang ; Yuqi Tan
• 关键词：Visual sensor network ; Optimal face acquisition ; 3D face reconstruction ; Self ; adaptive morphable model ; Particle swarm optimization
• 刊名：Multimedia Tools and Applications
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：75
• 期：18
• 页码：11469-11491
• 全文大小：3,402 KB
• 刊物类别：Computer Science
• 刊物主题：Multimedia Information Systems
  Computer Communication Networks
  Data Structures, Cryptology and Information Theory
  Special Purpose and Application-Based Systems
  
• 出版者：Springer Netherlands
• ISSN：1573-7721
• 卷排序：75

文摘

3D face reconstruction is an efficient method for pedestrian recognition in non-cooperative environment because of its outstanding performance in robust face recognition for uncontrolled pose and illumination changes. Visual sensor network is widely used in target surveillance as powerful unattended distributed measurement systems. This paper proposes a collaborative multi-view non-cooperative 3D face reconstruction method in visual sensor network. A peer-to-peer paradigm-based visual sensor network is employed for distributed pedestrian tracking and optimal face image acquisition. Gaussian probability distribution-based multi-view data fusion is used for target localization, and kalman filter is applied for target tracking. A lightweight face image quality evaluation method is presented to search optimal face images. A self-adaptive morphable model is designed for multiview 3D face reconstruction. To adjust the self-adaptive morphable model, the optimal face images and their poses estimation are used. Cooperative chaotic particle swarm optimization is employed for parameters optimization of the self-adaptive morphable model. Experimental results on real data show that the proposed method can acquire optimal face images and achieve non-cooperative 3D reconstruction efficiently.