Novel local feature extraction for age invariant face recognition

2021 ◽  
Vol 175 ◽  
pp. 114786
Author(s):  
Rajesh Kumar Tripathi ◽  
Anand Singh Jalal
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Local Feature ◽  
Local Feature Extraction

scholarly journals Monogenic Binary Coding: An Efficient Local Feature Extraction Approach to Face Recognition

2012 ◽  
Vol 7 (6) ◽  
pp. 1738-1751 ◽  
Author(s):  
Meng Yang ◽  
Lei Zhang ◽  
Simon Chi-Keung Shiu ◽  
David Zhang
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Local Feature ◽  
Binary Coding ◽  
Local Feature Extraction

scholarly journals Classification of Textures Using Filter Based Local Feature Extraction

2016 ◽  
Vol 75 ◽  
pp. 03001
Author(s):  
Veysel Gokhan Bocekci ◽  
Kazim Yildiz
Keyword(s):  
Feature Extraction ◽  
Local Feature ◽  
Local Feature Extraction

scholarly journals Memory-Augmented Transformer for Remote Sensing Image Semantic Segmentation

2021 ◽  
Vol 13 (22) ◽  
pp. 4518
Author(s):  
Xin Zhao ◽  
Jiayi Guo ◽  
Yueting Zhang ◽  
Yirong Wu
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Semantic Segmentation ◽  
Local Features ◽  
Local Feature ◽  
Global Information ◽  
Deep Convolutional Neural Networks ◽  
Global Representation ◽  
Local Feature Extraction ◽  
Global And Local

The semantic segmentation of remote sensing images requires distinguishing local regions of different classes and exploiting a uniform global representation of the same-class instances. Such requirements make it necessary for the segmentation methods to extract discriminative local features between different classes and to explore representative features for all instances of a given class. While common deep convolutional neural networks (DCNNs) can effectively focus on local features, they are limited by their receptive field to obtain consistent global information. In this paper, we propose a memory-augmented transformer (MAT) to effectively model both the local and global information. The feature extraction pipeline of the MAT is split into a memory-based global relationship guidance module and a local feature extraction module. The local feature extraction module mainly consists of a transformer, which is used to extract features from the input images. The global relationship guidance module maintains a memory bank for the consistent encoding of the global information. Global guidance is performed by memory interaction. Bidirectional information flow between the global and local branches is conducted by a memory-query module, as well as a memory-update module, respectively. Experiment results on the ISPRS Potsdam and ISPRS Vaihingen datasets demonstrated that our method can perform competitively with state-of-the-art methods.

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