scholarly journals An Efficient and Accurate Iris Recognition Algorithm Based on a Novel Condensed 2-ch Deep Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3721
Author(s):  
Guoyang Liu ◽  
Weidong Zhou ◽  
Lan Tian ◽  
Wei Liu ◽  
Yingjian Liu ◽  
...  
Keyword(s):  
High Performance ◽  
Iris Recognition ◽  
Recognition Algorithm ◽  
Learning Approaches ◽  
Large Database ◽  
Computational Burden ◽  
Automatic Feature Extraction ◽  
Training Samples ◽  
Gradient Based ◽  
Iris Identification

Recently, deep learning approaches, especially convolutional neural networks (CNNs), have attracted extensive attention in iris recognition. Though CNN-based approaches realize automatic feature extraction and achieve outstanding performance, they usually require more training samples and higher computational complexity than the classic methods. This work focuses on training a novel condensed 2-channel (2-ch) CNN with few training samples for efficient and accurate iris identification and verification. A multi-branch CNN with three well-designed online augmentation schemes and radial attention layers is first proposed as a high-performance basic iris classifier. Then, both branch pruning and channel pruning are achieved by analyzing the weight distribution of the model. Finally, fast finetuning is optionally applied, which can significantly improve the performance of the pruned CNN while alleviating the computational burden. In addition, we further investigate the encoding ability of 2-ch CNN and propose an efficient iris recognition scheme suitable for large database application scenarios. Moreover, the gradient-based analysis results indicate that the proposed algorithm is robust to various image contaminations. We comprehensively evaluated our algorithm on three publicly available iris databases for which the results proved satisfactory for real-time iris recognition.

A gradient-based automatic optimization CNN framework for EEG state recognition

2021 ◽  
Author(s):  
He Wang ◽  
Xinshan Zhu ◽  
Pinyin Chen ◽  
Yuxuan Yang ◽  
Chao Ma ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Search Algorithm ◽  
Search Space ◽  
Structure Design ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Automatic Feature Extraction ◽  
Model Search ◽  
Gradient Based

Abstract The Electroencephalogram (EEG) signal, as a data carrier that can contain a large amount of information about the human brain in different states, is one of the most widely used metrics for assessing human psychophysiological states. Among a variety of analysis methods, deep learning, especially convolutional neural network (CNN), has achieved remarkable results in recent years as a method to effectively extract features from EEG signals. Although deep learning has the advantages of automatic feature extraction and effective classification, it also faces difficulties in network structure design and requires an army of prior knowledge. Automating the design of these hyperparameters can therefore save experts' time and manpower. Neural architecture search techniques have thus emerged. In this paper, based on an existing gradient-based NAS algorithm, PC-DARTS, with targeted improvements and optimizations for the characteristics of EEG signals. Specifically, we establish the model architecture step by step based on the manually designed deep learning models for EEG discrimination by retaining the framework of the search algorithm and performing targeted optimization of the model search space. Corresponding features are extracted separately according to the frequency domain, time domain characteristics of the EEG signal and the spatial position of the EEG electrode. The architecture was applied to EEG-based emotion recognition and driver drowsiness assessment tasks. The results illustrate that compared with the existing methods, the model architecture obtained in this paper can achieve competitive overall accuracy and better standard deviation in both tasks. Therefore, this approach is an effective migration of NAS technology into the field of EEG analysis and has great potential to provide high-performance results for other types of classification and prediction tasks. This can effectively reduce the time cost for researchers and facilitate the application of CNN in more areas.

Texture Entropy-Based Classification for Iris Recognition Systems

2019 ◽  
Vol 29 (04) ◽  
pp. 2050051
Author(s):  
Veljko Papic ◽  
Jelena Krmar
Keyword(s):  
Iris Recognition ◽  
Large Scale ◽  
Search Space ◽  
Recognition System ◽  
Large Database ◽  
Person Identification ◽  
Texture Characteristics ◽  
Time Savings ◽  
Iris Identification

In recent years, iris recognition is one of the most widely used techniques for person identification. Automatic iris identification implies a comparison of query iris image with iris entries in a large database to determine the identity of the person. In this paper, we propose a straightforward and effective algorithm for the classification of irises into several categories according to the iris texture characteristics. The goal of the classification is to identify and retrieve a smaller subset of the large database and to narrow down the search space. In this way, the response time of the iris recognition system could be significantly improved. We analyzed several cases for dividing the whole database (we used UPOL, CASIA, and UBIRIS databases) into up to eight subsets and calculated the time savings. The simulation results illustrate the potential of the proposed classification method for large-scale iris databases.

scholarly journals Supervised Domain Adaptation for Automated Semantic Segmentation of the Atrial Cavity

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 898
Author(s):  
Marta Saiz-Vivó ◽  
Adrián Colomer ◽  
Carles Fonfría ◽  
Luis Martí-Bonmatí ◽  
Valery Naranjo
Keyword(s):  
High Performance ◽  
Computational Models ◽  
Domain Adaptation ◽  
Semantic Segmentation ◽  
Patient Specific ◽  
Mr Images ◽  
Training Samples ◽  
Volumetric Images ◽  
Acquisition Costs ◽  
Left And Right

Atrial fibrillation (AF) is the most common cardiac arrhythmia. At present, cardiac ablation is the main treatment procedure for AF. To guide and plan this procedure, it is essential for clinicians to obtain patient-specific 3D geometrical models of the atria. For this, there is an interest in automatic image segmentation algorithms, such as deep learning (DL) methods, as opposed to manual segmentation, an error-prone and time-consuming method. However, to optimize DL algorithms, many annotated examples are required, increasing acquisition costs. The aim of this work is to develop automatic and high-performance computational models for left and right atrium (LA and RA) segmentation from a few labelled MRI volumetric images with a 3D Dual U-Net algorithm. For this, a supervised domain adaptation (SDA) method is introduced to infer knowledge from late gadolinium enhanced (LGE) MRI volumetric training samples (80 LA annotated samples) to a network trained with balanced steady-state free precession (bSSFP) MR images of limited number of annotations (19 RA and LA annotated samples). The resulting knowledge-transferred model SDA outperformed the same network trained from scratch in both RA (Dice equals 0.9160) and LA (Dice equals 0.8813) segmentation tasks.

New active learning algorithms for near-infrared spectroscopy in agricultural applications

2021 ◽  
Vol 69 (4) ◽  
pp. 297-306
Author(s):  
Julius Krause ◽  
Maurice Günder ◽  
Daniel Schulz ◽  
Robin Gruna
Keyword(s):  
Active Learning ◽  
Near Infrared ◽  
Agricultural Products ◽  
Training Data ◽  
Calibration Model ◽  
Learning Approaches ◽  
Training Samples ◽  
Agricultural Applications ◽  
Selection Of

Abstract The selection of training data determines the quality of a chemometric calibration model. In order to cover the entire parameter space of known influencing parameters, an experimental design is usually created. Nevertheless, even with a carefully prepared Design of Experiment (DoE), redundant reference analyses are often performed during the analysis of agricultural products. Because the number of possible reference analyses is usually very limited, the presented active learning approaches are intended to provide a tool for better selection of training samples.

Iris Recognition Method Based on Feature Discrimination and Category Correlation

2014 ◽  
Vol 602-605 ◽  
pp. 1610-1613
Author(s):  
Ming Hai Yao ◽  
Na Wang ◽  
Jin Song Li
Keyword(s):  
Iris Recognition ◽  
Predictive Accuracy ◽  
Gabor Filter ◽  
Recognition Algorithm ◽  
Recognition Method ◽  
Texture Characteristic ◽  
Multi Scale ◽  
Laplacian Score ◽  
Circular Gabor Filter ◽  
Academy Of Sciences

With the increasing number of internet user, the authentication technology is more and more important. Iris recognition as an important method for identification, which has been attention by researchers. In order to improve the predictive accuracy of iris recognition algorithm, the iris recognition method is proposed based feature discrimination and category correlation. The feature discrimination and category correlation are calculated by laplacian score and mutual information. The formula about feature discrimination and category correlation are built. Aiming at texture characteristic of iris image, the multi-scale circular Gabor filter is used to feature extraction. The computational efficiency of algorithm is improved. In order to verify the validity of the algorithm, the CASIA iris database of Chinese Academy of Sciences is used to do the experiment. The experimental results show that our method has high predictive accuracy.

scholarly journals A new data classification improvement approach based on kernel clustering

2021 ◽  
Vol 2082 (1) ◽  
pp. 012021
Author(s):  
Bingsen Guo
Keyword(s):  
High Performance ◽  
Real Life ◽  
Data Classification ◽  
Classification Performance ◽  
Training Set ◽  
Kernel Clustering ◽  
Training Samples ◽  
Critical Issues ◽  
Extensive Performance ◽  
Query Sample

Abstract Data classification is one of the most critical issues in data mining with a large number of real-life applications. In many practical classification issues, there are various forms of anomalies in the real dataset. For example, the training set contains outliers, often enough to confuse the classifier and reduce its ability to learn from the data. In this paper, we propose a new data classification improvement approach based on kernel clustering. The proposed method can improve the classification performance by optimizing the training set. We first use the existing kernel clustering method to cluster the training set and optimize it based on the similarity between the training samples in each class and the corresponding class center. Then, the optimized reliable training set is trained to the standard classifier in the kernel space to classify each query sample. Extensive performance analysis shows that the proposed method achieves high performance, thus improving the classifier’s effectiveness.

scholarly journals Environmental Sound Recognition on Embedded Systems: From FPGAs to TPUs

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2622
Author(s):  
Jurgen Vandendriessche ◽  
Nick Wouters ◽  
Bruno da Silva ◽  
Mimoun Lamrini ◽  
Mohamed Yassin Chkouri ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Machine Learning Techniques ◽  
Sound Recognition ◽  
Learning Approaches ◽  
Environmental Sound ◽  
Embedded Devices ◽  
Power Efficient ◽  
Computationally Intensive ◽  
Environmental Sound Recognition

In recent years, Environmental Sound Recognition (ESR) has become a relevant capability for urban monitoring applications. The techniques for automated sound recognition often rely on machine learning approaches, which have increased in complexity in order to achieve higher accuracy. Nonetheless, such machine learning techniques often have to be deployed on resource and power-constrained embedded devices, which has become a challenge with the adoption of deep learning approaches based on Convolutional Neural Networks (CNNs). Field-Programmable Gate Arrays (FPGAs) are power efficient and highly suitable for computationally intensive algorithms like CNNs. By fully exploiting their parallel nature, they have the potential to accelerate the inference time as compared to other embedded devices. Similarly, dedicated architectures to accelerate Artificial Intelligence (AI) such as Tensor Processing Units (TPUs) promise to deliver high accuracy while achieving high performance. In this work, we evaluate existing tool flows to deploy CNN models on FPGAs as well as on TPU platforms. We propose and adjust several CNN-based sound classifiers to be embedded on such hardware accelerators. The results demonstrate the maturity of the existing tools and how FPGAs can be exploited to outperform TPUs.

A Wearable Device for Assisting Persons With Vision Impairment

2017 ◽  
Author(s):  
Urvish Trivedi ◽  
Jonielle McDonnough ◽  
Muhaimen Shamsi ◽  
Andrez Izurieta Ochoa ◽  
Alec Braynen ◽  
...  
Keyword(s):  
High Performance ◽  
Difficult Problem ◽  
Recognition Algorithm ◽  
Assistive Technologies ◽  
Baseline Data ◽  
Sensor Data ◽  
Vibrotactile Stimulation ◽  
High Definition ◽  
Ultrasonic Sensors ◽  
Novel Approach

Detecting humans and objects during walking has been a very difficult problem for people with visual impairment. To safely avoid collision with any object or human and to navigate from one location to another, it is significant to know how far and what kind of obstacle the user is facing. In recent years, many researches have shown that providing different vibration stimulation can be very useful to convey important information to the user. In this paper, we present our stereovision system with high definition camera to detect and identify humans and obstacles in real time and compare it with a modified version of existing wearable haptic belt that uses high-performance Ultrasonic sensors. The aim of this paper is to present the practicability of stereovision system over cane and assistive technology such as vibrotactile belt. The study is based on two assistive technologies. The first one consists of the vibrotactile belt connected to ultrasonic sensors and an accelerometer which returns user movement & speed information to the microcontroller. The microcontroller initiates expressive vibrotactile stimulation based on sensor data. Data gathered from this technology will be used as the baseline data for comparison with our stereovision system. Second, we present a novel approach to detect the type of obstacle using object recognition algorithm and the best approach to avoid it using the stereovision feedback. Data gathered from this technology with be comparted against the baseline data from the vibrotactile belt. In addition, we present the results of the comparative study which shows that stereovision system has plethora of advantages over vibrotactile belt.

The Two-Stage Recognition Method Based on Texture Signals of the Heterogeneous Unsteady Iris

2021 ◽  
Author(s):  
Shuai Liu ◽  
Yuanning Liu ◽  
Xiaodong Zhu ◽  
Jing Liu ◽  
Guang Huo ◽  
...  
Keyword(s):  
Iris Recognition ◽  
Recognition Accuracy ◽  
Texture Features ◽  
Unsteady State ◽  
Recognition Method ◽  
Two Stage ◽  
Second Stage ◽  
Highly Efficient ◽  
Training Samples ◽  
Gray Values

In this paper, a two-stage multi-category recognition structure based on texture features is proposed. This method can solve the problem of the decline in recognition accuracy in the scene of lightweight training samples. Besides, the problem of recognition effect different in the same recognition structure caused by the unsteady iris can also be solved. In this paper’s structure, digitized values of the edge shape in the iris texture of the image are set as the texture trend feature, while the differences between the gray values of the image obtained by convolution are set as the grayscale difference feature. Furthermore, the texture trend feature is used in the first-stage recognition. The template category that does not match the tested iris is the elimination category, and the remaining categories are uncertain categories. Whereas, in the second-stage recognition, uncertain categories are adopted to determine the iris recognition conclusion through the grayscale difference feature. Then, the experiment results using the JLU iris library show that the method in this paper can be highly efficient in multi-category heterogeneous iris recognition under lightweight training samples and unsteady state.

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