.png){kind=icon}
返回
Sparse graphical representation based discriminant analysis for heterogeneous face recognition
.png){kind=icon}
OA
AI
DOI：10.1016/j.sigpro.2018.10.015.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Face images captured in heterogeneous environments, e.g., sketches generated by the artists or composite generation software, photos taken by common cameras and infrared images captured by corresponding infrared imaging devices, are usually subject to large texture (i.e., style) differences. This results in heavily degraded performance of conventional face recognition methods directly applied on heterogeneous face images. In this paper, we propose a novel sparse graphical representation based discriminant analysis (SGR-DA) approach to address aforementioned cross-modality face recognition scenarios. An adaptive sparse graphical representation scheme is designed to represent face images from different modalities, where a Markov networks model is constructed to generate adaptive sparse vectors. To handle the complex facial structure and further improve the discriminability, a spatial partition-based discriminant analysis framework is presented to refine the adaptive sparse vectors for face matching. We conducted experiments on six commonly used heterogeneous face datasets and experimental comparison with both traditional and deep learning based approaches illustrated the superiority of our proposed SGR-DA. (C) 2018 Elsevier B.V. All rights reserved.
Keyword:
Heterogeneous face recognition
Discriminant analysis
Viewed sketch
Forensic sketch
Composite sketch
Infrared image
Thermal image
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
10.0K
被引数:
1.7W
机构
引用论文
分享收藏Common Marker Genes Identified from Various Sample Types for Systemic Lupus Erythematosus.png)
PLOS ONE
OAAI
PLOS ONEIF0
OAAI分享收藏Analysis of TRPV channel activation by stimulation of FCεRI and MRGPR receptors in mouse peritoneal mast cellsPLOS ONEOAAI
PLOS ONEIF0
OAAI分享收藏
查看更多内容