.png){kind=icon}
返回
Depth as Attention for Face Representation Learning
.png){kind=icon}
OA
AI
DOI：10.1109/TIFS.2021.3053458.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Face representation learning solutions have recently achieved great success for various applications such as verification and identification. However, face recognition approaches that are based purely on RGB images rely solely on intensity information, and therefore are more sensitive to facial variations, notably pose, occlusions, and environmental changes such as illumination and background. A novel depth-guided attention mechanism is proposed for deep multi-modal face recognition using low-cost RGB-D sensors. Our novel attention mechanism directs the deep network where to look for visual features in the RGB image by focusing the attention of the network using depth features extracted by a Convolution Neural Network (CNN). The depth features help the network focus on regions of the face in the RGB image that contain more prominent person-specific information. Our attention mechanism then uses this correlation to generate an attention map for RGB images from the depth features extracted by the CNN. We test our network on four public datasets, showing that the features obtained by our proposed solution yield better results on the Lock3DFace, CurtinFaces, IIIT-D RGB-D, and KaspAROV datasets which include challenging variations in pose, occlusion, illumination, expression, and time lapse. Our solution achieves average (increased) accuracies of 87.3% (+5.0%), 99.1% (+0.9%), 99.7% (+0.6%) and 95.3%(+0.5%) for the four datasets respectively, thereby improving the state-of-the-art. We also perform additional experiments with thermal images, instead of depth images, showing the high generalization ability of our solution when adopting other modalities for guiding the attention mechanism instead of depth information.
Keyword:
Face recognition
Feature extraction
Deep learning
Training
Lighting
Support vector machines
Resource description framework
RGB-D face recognition
depth-guided features
attention
multimodal deep network
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:
8
论文数:
5.3K
被引数:
2.3W
机构
引用论文
An RGB-D based image set classification for robust face recognition from Kinect data基于rgb-d的图像集分类，用于从Kinect数据中进行鲁棒的人脸识别.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏
Caw’s Walking State Recognition Based on Accelerometers and Gyroscopes Installed on Ear-Tags and Collar-Tags基于安装在耳标和项圈上的加速度计和陀螺仪的Caw步行状态识别
PREAI
分享收藏分享收藏
查看更多内容