scholarly journals Palmprint Recognition Based on Convolutional Neural Network-Alexnet

2019 ◽  
Author(s):  
Weiyong Gong ◽  
Xinman Zhang ◽  
Bohua Deng ◽  
Xuebin Xu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Palmprint Recognition

scholarly journals Genetic Algorithm Based Gabor CNN For Palmprint Recognition

2020 ◽  
Vol 8 (6) ◽  
pp. 4895-4899
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Convolutional Neural Network ◽  
Gabor Filter ◽  
Fitness Function ◽  
Palmprint Recognition ◽  
Central Frequency ◽  
The Past ◽  
Tremendous Progress ◽  
2D Gabor Filter

In the field of biometrics, palmprint recognition has received great interest and made tremendous progress in the past two decades. In palmprint recognition, the important step is to extract the discriminative features from the image and compare it with templates for identification and verification tasks. In this paper, a new genetic-based 2D Gabor filter with the Convolutional Neural Network is presented. The scale and orientation details captured by Gabor filters are optimized based on central frequency, which is determined based on genetic algorithm fitness function. The proposed technique is implemented on four publicly available palmprint datasets- PolyU, CASIA, IITD, and Tongji. Experimental results confirm that the proposed technique achieves better accuracy when compared to Palmnet.

scholarly journals HandSegNet: Hand segmentation using convolutional neural network for contactless palmprint recognition

2021 ◽  
Author(s):  
Koichi Ito ◽  
Yusei Suzuki ◽  
Hiroya Kawai ◽  
Takafumi Aoki ◽  
Masakazu Fujio ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Palmprint Recognition ◽  
Hand Segmentation

CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

2020 ◽  
Author(s):  
S Kashin ◽  
D Zavyalov ◽  
A Rusakov ◽  
V Khryashchev ◽  
A Lebedev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

Sign in / Sign up

Export Citation Format

Share Document