An improved low-complexity DenseUnet for high-accuracy iris segmentation network

2021 ◽  
pp. 1-17
Author(s):  
Weibin Zhou ◽  
Tao Chen ◽  
Huafang Huang ◽  
Chang Sheng ◽  
Yangfeng Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Iris Recognition ◽  
Low Complexity ◽  
High Accuracy ◽  
Iris Segmentation ◽  
High Complexity ◽  
Feature Maps ◽  
Segmentation Accuracy ◽  
Experimental Stage

Iris segmentation is one of the most important steps in iris recognition. The current iris segmentation network is based on convolutional neural network (CNN). Among these methods, there are still problems with the segmentation networks such as high complexity, insufficient accuracy, etc. To solve these problems, an improved low complexity DenseUnet is proposed to this paper based on U-net for acquiring a high-accuracy iris segmentation network. In this network, the improvements are as follows: (1) Design a dense block module that contains five convolutional layers and all convolutions are dilated convolutions aimed at enhancing feature extraction; (2) Except for the last convolutional layer, all convolutional layers output feature maps are set to the number 64, and this operation is to reduce the amounts of parameters without affecting the segmentation accuracy; (3) The solution proposed to this paper has low complexity and provides the possibility for the deployment of portable mobile devices. DenseUnet is used on the dataset of IITD, CASIA V4.0 and UBIRIS V2.0 during the experimental stage. The results of the experiments have shown that the iris segmentation network proposed in this paper has a better performance than existing algorithms.

scholarly journals Convolutional Neural Network for Iris Recognition

2021 ◽  
Author(s):  
Wael Alnahari
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Iris Recognition ◽  
Recognition System ◽  
High Accuracy ◽  
Accuracy Rate

Abstract In this paper, I proposed an iris recognition system by using deep learning via neural networks (CNN). Although CNN is used for machine learning, the recognition is achieved by building a non-trained CNN network with multiple layers. The main objective of the code the test pictures’ category (aka person name) with a high accuracy rate after having extracted enough features from training pictures of the same category which are obtained from a that I added to the code. I used IITD iris which included 10 iris pictures for 223 people.

scholarly journals DMCNN: A Deep Multiscale Convolutional Neural Network Model for Medical Image Segmentation

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Lin Teng ◽  
Hang Li ◽  
Shahid Karim
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Image Segmentation ◽  
Convolutional Neural Network ◽  
Medical Image ◽  
Data Augmentation ◽  
Medical Images ◽  
Medical Image Segmentation ◽  
Related Area ◽  
Segmentation Accuracy

Medical image segmentation is one of the hot issues in the related area of image processing. Precise segmentation for medical images is a vital guarantee for follow-up treatment. At present, however, low gray contrast and blurred tissue boundaries are common in medical images, and the segmentation accuracy of medical images cannot be effectively improved. Especially, deep learning methods need more training samples, which lead to time-consuming process. Therefore, we propose a novelty model for medical image segmentation based on deep multiscale convolutional neural network (CNN) in this article. First, we extract the region of interest from the raw medical images. Then, data augmentation is operated to acquire more training datasets. Our proposed method contains three models: encoder, U-net, and decoder. Encoder is mainly responsible for feature extraction of 2D image slice. The U-net cascades the features of each block of the encoder with those obtained by deconvolution in the decoder under different scales. The decoding is mainly responsible for the upsampling of the feature graph after feature extraction of each group. Simulation results show that the new method can boost the segmentation accuracy. And, it has strong robustness compared with other segmentation methods.

scholarly journals Noncircular Iris Segmentation Based on Weighted Adaptive Hough Transform Using Smartphone Database

2018 ◽  
Vol 15 (2) ◽  
pp. 739-743 ◽  
Author(s):  
Noor Amjed ◽  
Fatimah Khalid ◽  
Rahmita Wirza O. K. Rahmat ◽  
Hizmawati Binit Madzin
Keyword(s):  
Hough Transform ◽  
Iris Recognition ◽  
Recognition System ◽  
Iris Segmentation ◽  
Negative Image ◽  
Processing Step ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
High Level ◽  
Imaging Conditions

Iris segmentation methods work based on ideal imaging conditions which produce good output results. However, the segmentation accuracy of an iris recognition system significantly influences its performance, especially with data that captured in unconstrained environment of the Smartphone. This paper proposes a novel segmentation method for unconstrained environment of the Smartphone videos based on choose the best frames from the videos and try to enhance the contrast of this frames by applying the two fuzzy logic membership functions on the negative image which delimit between dark and bright regions in able to make the dark region darker and the bright region brighter. This pre-processing step Facilitates the work of the Weighted Adaptive Hough Transform to automatically find the diameter of the iris region to apply the osiris v4.1. The proposed method results on the video of (Mobile Iris Challenge Evaluation (MICHE))-I, iris databases indicate a high level of accuracy and more efficient computationally using the proposed technique.

scholarly journals Super-resolution Ultrasound Imaging Scheme Based on a Symmetric Series Convolutional Neural Network

2021 ◽  
Author(s):  
Lakpa Dorje Tamang
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Ultrasound Image ◽  
Similarity Index ◽  
Super Resolution ◽  
Input Image ◽  
Feature Maps ◽  
Proposed Model ◽  
Reconstruction Performance

In this paper, we propose a symmetric series convolutional neural network (SS-CNN), which is a novel deep convolutional neural network (DCNN)-based super-resolution (SR) technique for ultrasound medical imaging. The proposed model comprises two parts: a feature extraction network (FEN) and an up-sampling layer. In the FEN, the low-resolution (LR) counterpart of the ultrasound image passes through a symmetric series of two different DCNNs. The low-level feature maps obtained from the subsequent layers of both DCNNs are concatenated in a feed forward manner, aiding in robust feature extraction to ensure high reconstruction quality. Subsequently, the final concatenated features serve as an input map to the latter 2D convolutional layers, where the textural information of the input image is connected via skip connections. The second part of the proposed model is a sub-pixel convolutional (SPC) layer, which up-samples the output of the FEN by multiplying it with a multi-dimensional kernel followed by a periodic shuffling operation to reconstruct a high-quality SR ultrasound image. We validate the performance of the SS-CNN with publicly available ultrasound image datasets. Experimental results show that the proposed model achieves an exquisite reconstruction performance of ultrasound image over the conventional methods in terms of peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM), while providing compelling SR reconstruction time.

scholarly journals Recognition of Scratches and Abrasions on Metal Surfaces Using a Classifier Based on a Convolutional Neural Network

Metals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 549
Author(s):  
Ihor Konovalenko ◽  
Pavlo Maruschak ◽  
Vitaly Brevus ◽  
Olegas Prentkovskis
Keyword(s):  
Neural Network ◽  
Steel Industry ◽  
Network Architecture ◽  
Metal Surfaces ◽  
Surface Defects ◽  
High Accuracy ◽  
Feature Maps ◽  
Neural Network Architecture ◽  
Neuron Activation

Classification of steel surface defects in steel industry is essential for their detection and also fundamental for the analysis of causes that lead to damages. Timely detection of defects allows to reduce the frequency of their appearance in the final product. This paper considers the classifiers for the recognition of scratches, scrapes and abrasions on metal surfaces. Classifiers are based on the ResNet50 and ResNet152 deep residual neural network architecture. The proposed technique supports the recognition of defects in images and does this with high accuracy. The binary accuracy of the classification based on the test data is 97.14%. The influence of a number of training conditions on the accuracy metrics of the model have been studied. The augmentation conditions have been figured out to make the greatest contribution to improving the accuracy during training. The peculiarities of damages that cause difficulties in their recognition have been studied. The fields of neuron activation have been investigated in the convolutional layers of the model. Feature maps which developed in this case have been found to correspond to the location of the objects of interest. Erroneous cases of the classifier application have been considered. The peculiarities of damages that cause difficulties in their recognition have been studied.

Noisy Iris Segmentation with Reflections Removal Using Probable Boundary Edge Detector

2012 ◽  
Vol 236-237 ◽  
pp. 1116-1121 ◽  
Author(s):  
Min Wang ◽  
Ning Wang ◽  
Xiao Gui Yao
Keyword(s):  
Iris Recognition ◽  
State Of The Art ◽  
Image Database ◽  
Recognition System ◽  
Experimental Results ◽  
Boundary Edge ◽  
Iris Segmentation ◽  
Edge Detector ◽  
Segmentation Methods ◽  
Segmentation Accuracy

Iris segmentation plays an important role in iris recognition system. Most of segmentation methods are affected by reflection spots, eyelash and eyelid etc. The goal of this work is to accurately segment the iris using Probable boundary (Pb) edge detector after horizontal-vertical weighted reflections removal. Experimental results on the challenging iris image database CASIA-Iris-Thousand with reflection spots sample demonstrate that the iris segmentation accuracy of the proposed methods outperforms state-of-the-art methods.

scholarly journals A fast specular reflection removal based on pixels properties method

2020 ◽  
Vol 9 (6) ◽  
pp. 2358-2363
Author(s):  
Shahrizan Jamaludin ◽  
Nasharuddin Zainal ◽  
W. Mimi Diyana W. Zaki
Keyword(s):  
Execution Time ◽  
Iris Recognition ◽  
Specular Reflection ◽  
Recognition System ◽  
The Other ◽  
Iris Segmentation ◽  
Removal Method ◽  
Human Iris ◽  
Segmentation Accuracy ◽  
Recognition Systems

Iris recognition has been around for many years due to an extensive research on the uniqueness of human iris. It is well known that the iris is not similar to each other which means every human in the planet has their own iris pattern and cannot be shared. One of the main issues in iris recognition is iris segmentation. One element that can reduce the accuracy of iris segmentation is the presence of specular reflection. Another issue is the speed of specular reflection removal since the iris recognition system needs to process a lot of irises. In this paper, a specular reflection removal method was proposed to achieve a fast and accurate specular reflection removal. Some modifications were implemented on the existing pixels properties method. Based on the results, the proposed method achieved the fastest execution time, the highest segmentation accuracy and the highest SSIM compared to the other methods. This proves that the proposed method is fast and accurate to be implemented in the iris recognition systems.

scholarly journals SFRNet: Feature Extraction-Fusion Steganalysis Network Based on Squeeze-and-Excitation Block and RepVgg Block

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Guiyong Xu ◽  
Yang Xu ◽  
Sicong Zhang ◽  
Xiaoyao Xie
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Detection Accuracy ◽  
Image Size ◽  
Feature Maps ◽  
Accuracy Rate ◽  
Excellent Performance ◽  
Training Time

In the era of big data, convolutional neural network (CNN) has been widely used in the field of image classification and has achieved excellent performance. More and more researchers are beginning to combine deep neural networks with steganalysis to improve performance in recent years. However, most of the steganalysis algorithm based on the convolutional neural network has only run test against the WOW and S-UNIWARD algorithms; meanwhile, their versatility is insufficient due to long training time and the limit of image size. This paper proposes a new network architecture, called SFRNet, to solve these problems. The feature extraction and fusion layer can extract more features from the digital image. The RepVgg block is used to accelerate the inference and increase memory utilization. The SE block improves the detection accuracy rate because it can learn feature weights to make effective feature maps with significant weights and invalid or ineffective feature maps with small weights. Experimental results show that the SFRNet has achieved excellent performance in the detection accuracy rate against four state-of-the-art steganography algorithms in the spatial domain, e.g., HUGO, WOW, S-UNIWARD, and MiPOD, under different payloads. The SFRNet detection accuracy rate achieves 89.6% against S-UNIWARD algorithm with the payload of 0.4bpp and 72.5% at 0.2bpp. As the same time, the training time of our network is greatly reduced by 35% compared with Yedroudj-Net.

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