Quick retrieval method of massive face images based on global feature and local feature fusion

2017 ◽  
Author(s):  
Wei Yu ◽  
Qiuyu Zhu
Keyword(s):  
Feature Fusion ◽  
Local Feature ◽  
Global Feature ◽  
Retrieval Method ◽  
Face Images

scholarly journals Structure-Aware Feature Fusion for Unsupervised Domain Adaptation

2020 ◽  
Vol 34 (07) ◽  
pp. 10567-10574
Author(s):  
Qingchao Chen ◽  
Yang Liu
Keyword(s):  
Spatial Structure ◽  
Feature Fusion ◽  
Domain Adaptation ◽  
Structural Information ◽  
Feature Representation ◽  
Local Feature ◽  
Global Feature ◽  
Feature Maps ◽  
Fine Grained ◽  
Unsupervised Domain Adaptation

Unsupervised domain Adaptation (UDA) aims to learn and transfer generalized features from a labelled source domain to a target domain without any annotations. Existing methods only aligning high-level representation but without exploiting the complex multi-class structure and local spatial structure. This is problematic as 1) the model is prone to negative transfer when the features from different classes are misaligned; 2) missing the local spatial structure poses a major obstacle in performing the fine-grained feature alignment. In this paper, we integrate the valuable information conveyed in classifier prediction and local feature maps into global feature representation and then perform a single mini-max game to make it domain invariant. In this way, the domain-invariant feature not only describes the holistic representation of the original image but also preserves mode-structure and fine-grained spatial structural information. The feature integration is achieved by estimating and maximizing the mutual information (MI) among the global feature, local feature and classifier prediction simultaneously. As the MI is hard to measure directly in high-dimension spaces, we adopt a new objective function that implicitly maximizes the MI via an effective sampling strategy and a discriminator design. Our STructure-Aware Feature Fusion (STAFF) network achieves the state-of-the-art performances in various UDA datasets.

Sewer pipe defect detection via deep learning with local and global feature fusion

2021 ◽  
Vol 129 ◽  
pp. 103823
Author(s):  
Dawei Li ◽  
Qian Xie ◽  
Zhenghao Yu ◽  
Qiaoyun Wu ◽  
Jun Zhou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Feature Fusion ◽  
Global Feature ◽  
Sewer Pipe

scholarly journals Face Liveness Detection using Feature Fusion Using Block Truncation Code Technique

2019 ◽  
Vol 7 (8) ◽  
pp. 19-22
Author(s):  
Prasad A. Jagdale ◽  
Sudeep D. Thepade
Keyword(s):  
Face Recognition ◽  
Feature Fusion ◽  
Recognition System ◽  
Security System ◽  
Major Drawback ◽  
Liveness Detection ◽  
Face Images ◽  
Face Recognition System ◽  
Confidential Data ◽  
Finger Recognition

Nowadays the system which holds private and confidential data are being protected using biometric password such as finger recognition, voice recognition, eyries and face recognition. Face recognition match the current user face with faces present in the database of that security system and it has one major drawback that it never works better if it doesn’t have liveness detection. These face recognition system can be spoofed using various traits. Spoofing is accessing a system software or data by harming the biometric recognition security system. These biometric systems can be easily attacked by spoofs like peoples face images, masks and videos which are easily available from social media. The proposed work mainly focused on detecting the spoofing attack by training the system. Spoofing methods like photo, mask or video image can be easily identified by this method. This paper proposed a fusion technique where different features of an image are combining together so that it can give best accuracy in terms of distinguish between spoof and live face. Also a comparative study is done of machine learning classifiers to find out which classifiers gives best accuracy.

Lightweight feature fusion network design for local feature recognition of non-cooperative target

2020 ◽  
Vol 49 (7) ◽  
pp. 20200170
Author(s):  
徐云飞 Yunfei Xu ◽  
张笃周 Duzhou Zhang ◽  
王立 Li Wang ◽  
华宝成 Baocheng Hua
Keyword(s):  
Network Design ◽  
Feature Recognition ◽  
Feature Fusion ◽  
Local Feature

Exploiting Local Feature Fusion for Action Recognition

2016 ◽  
pp. 221-230
Author(s):  
Jie Miao ◽  
Xiangmin Xu ◽  
Xiaoyi Jia ◽  
Haoyu Huang ◽  
Bolun Cai ◽  
...  
Keyword(s):  
Action Recognition ◽  
Feature Fusion ◽  
Local Feature
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