scholarly journals Iris Localization Algorithm based on Effective Area

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jinfeng Yu ◽  
Lei Zhang ◽  
Zhi Wang
Keyword(s):  
State Of The Art ◽  
Outer Boundary ◽  
Effective Area ◽  
Localization Algorithm ◽  
Iris Localization ◽  
Localization Accuracy ◽  
Noise Interference ◽  
Boundary Determination ◽  
Crucial Part

Iris localization is the most crucial part of the iris processing because its accuracy can directly affect the accuracy of biometric identification in subsequent steps. Yet, the quality of iris images may be sharply degraded due to interference from eyelashes and reflections during image acquisition, which can affect the localization accuracy adversely. To solve the problem, an iris localization algorithm based on effective area is proposed. First, YOLOv4 is used to crop the image to obtain the effective iris area, which is beneficial in improving the accuracy of subsequent localization. Furthermore, a method to remove reflective noise is proposed, which can effectively avoid the problem of noise interference in the process of inner boundary determination. Finally, aiming at the edge deviation caused by eyelashes, an outer boundary adjustment method is proposed. The experimental results show that the proposed method achieves good performance in the localization of iris images of both good quality and noise interference and outperforms other state-of-the-art methods.

A ROBUST IRIS LOCALIZATION ALGORITHM VIA RADIAL SYMMETRY FOR NONIDEAL CAPTURING CONDITION

2009 ◽  
Vol 23 (01) ◽  
pp. 45-57 ◽  
Author(s):  
WEN-CONG ZHANG ◽  
BIN LI ◽  
XUE-YI YE ◽  
ZHEN-QUAN ZHUANG ◽  
KONG-QIAO WANG
Keyword(s):  
Iris Recognition ◽  
Recognition System ◽  
Radial Symmetry ◽  
Precise Localization ◽  
Localization Algorithm ◽  
Time System ◽  
Iris Localization ◽  
Real Time System ◽  
The Ideal

Iris localization is a key component of practical iris recognition system. Previous algorithms show good localization performances for iris images captured in the ideal conditions. However, in practice, the quality of iris image is greatly influenced by luminance, eyelashes, hair or glasses frame, which will cause mislocalization. In order to improve the robustness of iris localization, this paper proposes a new localization algorithm based on the radial symmetry transform, in which the radial symmetry characteristic of the pupil is utilized to realize iris localization. Experimental results show that the proposed algorithm can efficiently avoid the interference of luminance and other bad conditions, and realize robust precise localization in a real-time system.

An Improved Iris Localization Algorithm Based on Curve Fitting

2013 ◽  
Vol 380-384 ◽  
pp. 1176-1179
Author(s):  
Yi Huang ◽  
Xiao Ping Zeng
Keyword(s):  
Curve Fitting ◽  
Outer Boundary ◽  
Regions Of Interest ◽  
Localization Algorithm ◽  
Iris Localization ◽  
Iris Image ◽  
The Third ◽  
Boundary Points ◽  
Maximum Variance ◽  
Improved Algorithm

Iris localization is to detect outer-and-inner boundaries of iris in an iris image. In the paper, an improved algorithm was proposed to quickly and effectively locate outer-and-inner boundaries. As for this algorithm, the first is to block an iris image and extract its sub-image blocks which cover pupil; the second is to set a binary threshold of pupil by adopting the method of Maximum Variance between Clusters; the third is to get the value outer-boundary-points of iris, on the basis of gray gradient of key Regions-of-interest; the last is to select some characteristic pixels in regions of interest respectively and fit outer-and-inner boundaries of iris according to curve fitting.

scholarly journals Efficient Iris Localization via Optimization Model

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Qi Wang ◽  
Zhipeng Liu ◽  
Shu Tong ◽  
Yuqi Yang ◽  
Xiangde Zhang
Keyword(s):  
Optimization Model ◽  
Iris Recognition ◽  
Outer Boundary ◽  
Descent Method ◽  
Least Square ◽  
Experimental Result ◽  
Localization Problem ◽  
Localization Algorithm ◽  
Iris Localization ◽  
Supervised Descent Method

Iris localization is one of the most important processes in iris recognition. Because of different kinds of noises in iris image, the localization result may be wrong. Besides this, localization process is time-consuming. To solve these problems, this paper develops an efficient iris localization algorithm via optimization model. Firstly, the localization problem is modeled by an optimization model. Then SIFT feature is selected to represent the characteristic information of iris outer boundary and eyelid for localization. And SDM (Supervised Descent Method) algorithm is employed to solve the final points of outer boundary and eyelids. Finally, IRLS (Iterative Reweighted Least-Square) is used to obtain the parameters of outer boundary and upper and lower eyelids. Experimental result indicates that the proposed algorithm is efficient and effective.

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

Improved fast iris localization algorithm

2008 ◽  
Vol 28 (3) ◽  
pp. 674-676 ◽  
Author(s):  
Liu-liu ZHU
Keyword(s):  
Localization Algorithm ◽  
Iris Localization

State-of-the-Art: A Systematic Literature Review on Image Segmentation in Latent Fingerprint Forensics

2019 ◽  
Vol 12 ◽  
Author(s):  
Megha Chhabra ◽  
Manoj Kumar Shukla ◽  
Kiran Kumar Ravulakollu
Keyword(s):  
Image Segmentation ◽  
Background Noise ◽  
State Of The Art ◽  
Recognition Rate ◽  
Poor Quality ◽  
Latent Fingerprint ◽  
In The Beginning ◽  
The Comparative Study ◽  
Finger Skin

: Latent fingerprints are unintentional finger skin impressions left as ridge patterns at crime scenes. A major challenge in latent fingerprint forensics is the poor quality of the lifted image from the crime scene. Forensics investigators are in permanent search of novel outbreaks of the effective technologies to capture and process low quality image. The accuracy of the results depends upon the quality of the image captured in the beginning, metrics used to assess the quality and thereafter level of enhancement required. The low quality of the image collected by low quality scanners, unstructured background noise, poor ridge quality, overlapping structured noise result in detection of false minutiae and hence reduce the recognition rate. Traditionally, Image segmentation and enhancement is partially done manually using help of highly skilled experts. Using automated systems for this work, differently challenging quality of images can be investigated faster. This survey amplifies the comparative study of various segmentation techniques available for latent fingerprint forensics.

UBAR

2021 ◽  
Vol 20 (3) ◽  
pp. 1-25
Author(s):  
Elham Shamsa ◽  
Alma Pröbstl ◽  
Nima TaheriNejad ◽  
Anil Kanduri ◽  
Samarjit Chakraborty ◽  
...  
Keyword(s):  
Resource Management ◽  
Quality Of Experience ◽  
State Of The Art ◽  
State Of Charge ◽  
User Preference ◽  
Management Approach ◽  
High Quality ◽  
Trade Off ◽  
Run Time

Smartphone users require high Battery Cycle Life (BCL) and high Quality of Experience (QoE) during their usage. These two objectives can be conflicting based on the user preference at run-time. Finding the best trade-off between QoE and BCL requires an intelligent resource management approach that considers and learns user preference at run-time. Current approaches focus on one of these two objectives and neglect the other, limiting their efficiency in meeting users’ needs. In this article, we present UBAR, User- and Battery-aware Resource management, which considers dynamic workload, user preference, and user plug-in/out pattern at run-time to provide a suitable trade-off between BCL and QoE. UBAR personalizes this trade-off by learning the user’s habits and using that to satisfy QoE, while considering battery temperature and State of Charge (SOC) pattern to maximize BCL. The evaluation results show that UBAR achieves 10% to 40% improvement compared to the existing state-of-the-art approaches.

scholarly journals Smoothing additive manufactured parts using ns-pulsed laser radiation

2021 ◽  
Author(s):  
Florian Kuisat ◽  
Fernando Lasagni ◽  
Andrés Fabián Lasagni
Keyword(s):  
Surface Roughness ◽  
Mechanical Performance ◽  
State Of The Art ◽  
Pulsed Laser ◽  
Industrial Applications ◽  
Laser Source ◽  
Pulsed Laser Radiation ◽  
Current State ◽  
The Impact

AbstractIt is well known that the surface topography of a part can affect its mechanical performance, which is typical in additive manufacturing. In this context, we report about the surface modification of additive manufactured components made of Titanium 64 (Ti64) and Scalmalloy®, using a pulsed laser, with the aim of reducing their surface roughness. In our experiments, a nanosecond-pulsed infrared laser source with variable pulse durations between 8 and 200 ns was applied. The impact of varying a large number of parameters on the surface quality of the smoothed areas was investigated. The results demonstrated a reduction of surface roughness Sa by more than 80% for Titanium 64 and by 65% for Scalmalloy® samples. This allows to extend the applicability of additive manufactured components beyond the current state of the art and break new ground for the application in various industrial applications such as in aerospace.

scholarly journals Ensemble-Based Out-of-Distribution Detection

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 567
Author(s):  
Donghun Yang ◽  
Kien Mai Mai Ngoc ◽  
Iksoo Shin ◽  
Kyong-Ha Lee ◽  
Myunggwon Hwang
Keyword(s):  
Detection Method ◽  
State Of The Art ◽  
Metric Learning ◽  
Feature Space ◽  
Confidence Score ◽  
Distance Metric Learning ◽  
Current State ◽  
Overall Performance ◽  
Deep Learning Model

To design an efficient deep learning model that can be used in the real-world, it is important to detect out-of-distribution (OOD) data well. Various studies have been conducted to solve the OOD problem. The current state-of-the-art approach uses a confidence score based on the Mahalanobis distance in a feature space. Although it outperformed the previous approaches, the results were sensitive to the quality of the trained model and the dataset complexity. Herein, we propose a novel OOD detection method that can train more efficient feature space for OOD detection. The proposed method uses an ensemble of the features trained using the softmax-based classifier and the network based on distance metric learning (DML). Through the complementary interaction of these two networks, the trained feature space has a more clumped distribution and can fit well on the Gaussian distribution by class. Therefore, OOD data can be efficiently detected by setting a threshold in the trained feature space. To evaluate the proposed method, we applied our method to various combinations of image datasets. The results show that the overall performance of the proposed approach is superior to those of other methods, including the state-of-the-art approach, on any combination of datasets.

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