Iris segmentation for non-ideal Iris biometric systems

2021 ◽  
Author(s):  
Farmanullah Jan ◽  
Saleh Alrashed ◽  
Nasro Min-Allah
Keyword(s):  
Iris Segmentation ◽  
Biometric Systems

Iris Segmentation for Non-ideal Images

2015 ◽  
Vol 74 (3) ◽  
Author(s):  
Nasharuddin Zainal ◽  
Abduljalil Radman ◽  
Mahamod Ismail ◽  
Md Jan Nordin
Keyword(s):  
Iris Recognition ◽  
Recognition Algorithm ◽  
Segmentation Method ◽  
Iris Segmentation ◽  
West Virginia University ◽  
Segmentation Methods ◽  
Biometric Systems ◽  
Segmentation Algorithms ◽  
Recognition Systems ◽  
Constrained Conditions

Iris recognition has been regarded as one of the most reliable biometric systems over the past years. Previous studies have shown that the performance of iris recognition systems highly dependent on the performance of their segmentation algorithms. Iris segmentation is the process to isolate the iris region from the surrounded structures of the eye image. However, several iris segmentation algorithms have been developed in the literature, but their segmentation and recognition accuracies drastically degrade with non-ideal iris images acquired in less constrained conditions. Thus, it is crucial to develop a new iris segmentation method to improve iris recognition using non-ideal images. Hence, the objective of this paper is an iris segmentation method on the basis of optimization to isolate the iris region from non-ideal iris images such those affected by reflections, blurred boundaries, eyelids occlusion, and gaze-deviation. Experimental results on the off axis/angle West Virginia University (WVU) iris database demonstrated the superiority of the developed method over state-of-the-art iris segmentation methods considered in this paper. The performance of an iris recognition algorithm based on the developed iris segmentation method was observed to be improved.  

Iris Segmentation Based on Improved U-Net Network Model

2019 ◽  
Vol E102.A (8) ◽  
pp. 982-985
Author(s):  
Chunhui GAO ◽  
Guorui FENG ◽  
Yanli REN ◽  
Lizhuang LIU
Keyword(s):  
Network Model ◽  
Iris Segmentation

Human Ear Pattern Recognition System

2017 ◽  
Vol 7 (8) ◽  
pp. 117 ◽  
Author(s):  
V. Jagan Naveen ◽  
K. Krishna Kishore ◽  
P. Rajesh Kumar
Keyword(s):  
Pattern Recognition ◽  
Expectation Maximization Algorithm ◽  
Recognition System ◽  
Modern World ◽  
Person Identification ◽  
Biometric Systems ◽  
Different Types ◽  
Recognition Systems ◽  
Human Ear ◽  
Pca Algorithm

In the modern world, human recognition systems play an important role to   improve security by reducing chances of evasion. Human ear is used for person identification .In the Empirical study on research on human ear, 10000 images are taken to find the uniqueness of the ear. Ear based system is one of the few biometric systems which can provides stable characteristics over the age. In this paper, ear images are taken from mathematical analysis of images (AMI) ear data base and the analysis is done on ear pattern recognition based on the Expectation maximization algorithm and k means algorithm.  Pattern of ears affected with different types of noises are recognized based on Principle component analysis (PCA) algorithm.

Non-Orthogonal Iris Segmentation

2005 ◽  
Author(s):  
Bradford L. Bonney
Keyword(s):  
Iris Segmentation

Multimodal biometric system using deep learning based on face and finger vein fusion

2021 ◽  
pp. 1-13
Author(s):  
Shikhar Tyagi ◽  
Bhavya Chawla ◽  
Rupav Jain ◽  
Smriti Srivastava
Keyword(s):  
High Performance ◽  
Recognition Accuracy ◽  
Error Rates ◽  
Facial Features ◽  
Biometric System ◽  
Deep Convolutional Neural Networks ◽  
Finger Vein ◽  
Biometric Systems ◽  
Overall Performance ◽  
Recognition Systems

Single biometric modalities like facial features and vein patterns despite being reliable characteristics show limitations that restrict them from offering high performance and robustness. Multimodal biometric systems have gained interest due to their ability to overcome the inherent limitations of the underlying single biometric modalities and generally have been shown to improve the overall performance for identification and recognition purposes. This paper proposes highly accurate and robust multimodal biometric identification as well as recognition systems based on fusion of face and finger vein modalities. The feature extraction for both face and finger vein is carried out by exploiting deep convolutional neural networks. The fusion process involves combining the extracted relevant features from the two modalities at score level. The experimental results over all considered public databases show a significant improvement in terms of identification and recognition accuracy as well as equal error rates.

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