scholarly journals Multi-Scale Fully Convolutional Network for Fast Face Detection

2016 ◽  
Author(s):  
Yancheng Bai ◽  
Wenjing Ma ◽  
Yucheng Li ◽  
Liangliang Cao ◽  
Wen Guo ◽  
...  
Keyword(s):  
Face Detection ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Multi Scale

A Part-Aware Multi-Scale Fully Convolutional Network for Pedestrian Detection

2020 ◽  
pp. 1-13
Author(s):  
Peiyu Yang ◽  
Guofeng Zhang ◽  
Lu Wang ◽  
Lisheng Xu ◽  
Qingxu Deng ◽  
...  
Keyword(s):  
Pedestrian Detection ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Multi Scale

scholarly journals FMnet: Iris Segmentation and Recognition by Using Fully and Multi-Scale CNN for Biometric Security

2019 ◽  
Vol 9 (10) ◽  
pp. 2042 ◽  
Author(s):  
Rachida Tobji ◽  
Wu Di ◽  
Naeem Ayoub
Keyword(s):  
Neural Networks ◽  
Iris Recognition ◽  
State Of The Art ◽  
Features Extraction ◽  
Iris Segmentation ◽  
Recognition Method ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Multi Scale ◽  
Biometric Security

In Deep Learning, recent works show that neural networks have a high potential in the field of biometric security. The advantage of using this type of architecture, in addition to being robust, is that the network learns the characteristic vectors by creating intelligent filters in an automatic way, grace to the layers of convolution. In this paper, we propose an algorithm “FMnet” for iris recognition by using Fully Convolutional Network (FCN) and Multi-scale Convolutional Neural Network (MCNN). By taking into considerations the property of Convolutional Neural Networks to learn and work at different resolutions, our proposed iris recognition method overcomes the existing issues in the classical methods which only use handcrafted features extraction, by performing features extraction and classification together. Our proposed algorithm shows better classification results as compared to the other state-of-the-art iris recognition approaches.

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