IWT Based Iris Recognition for Image Authentication

The development of technology has affected the performance of the healthcare system globally. There is no denying the way that security is fundamental in-patient consideration. Health care records are essential to every patient and medical expert as well. There should be a system that only authorised person can get access to the patient record because if any unauthorised person gains access to the patient record, it can lead to the wrong medication or even the death of the patient. Identification of a person is a very challenging problem. The traditional method includes ID cards and password etc. It can be lost, misuse or can share among colleague. To overcome these limitations of traditional ways biometric authentication method introduced. Biometric as the identifier can play a significant role in maintaining security and privacy of healthcare system. Biometric identification is the measurable unique traits of the individual. It divides into two categories: first is physiological that includes the face, fingerprints, iris, retina and hand geometry. Second is behavioural that includes voice recognition, signature verification and keystroke dynamics. In this survey paper, we review different physical and behavioral biometric techniques that described in a different research paper in past decades; then we analyse findings and future direction of different papers separately based on physical and behavioural biometric methods. We provide a comparative analysis of all biometric techniques it will help out readers to get a better idea about different biometric technologies that which one will suit according to their needs and budget. We conclude that for identification, authentication, in healthcare sector there is a need of proper implementation of any of these biometric authentication techniques or the combination of different techniques properly, to provide advanced security and prevention from any security threats to patient records.

Biometric Authentication in Health Care Sector: A Survey

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/bme2.12001

A multimodal retina-iris biometric system using the Levenshtein distance for spatial feature comparison

Mental condition and sentiment of a person can be analyzed through facial expressions. An emotion recognition system is proposed by recognizing facial expressions. Input images are preprocessed and then proposed image segmentation method is applied to segment a facial image into four parts that contribute highly in representing facial expressions. Features are extracted from the segmented parts using a fusion of Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP). The dimension of the feature vector is reduced using Principal Component Analysis (PCA). Finally, Artificial Neural Network (ANN) is used to classify the facial expressions properly. The proposed system is tested using three widely used facial expression datasets (JAFFE, CK +, RaFD). At last, the achieved performance is compared with other facial expression recognition systems to justify that the proposed method succeeds in achieving state-of-the-art performance.

A Facial Region Segmentation Based Approach to Recognize Human Emotion Using Fusion of HOG & LBP Features and Artificial Neural Network

As part of human emotion recognition, a As part of human emotion recognition, a facial expression recognition method is proposed where segmentation of facial regions are done manually in a unique yet effective way by analyzing many human faces and the position of the right eye, left eye, nose and mouth in those faces. Feature extraction from the segmented parts are done using 2D Gabor filter, redundant features are eliminated using downsampling of the extracted features and finally, classification of the expressions are done using K-Nearest Neighbors (KNN) classification technique. To evaluate the performance of the proposed method CK+, RaFD and KDEF datasets are used. For the CK+, RaFD and KDEF dataset the recognition rates are 99.75%, 94.60% and 86.02% respectively which indicates the effectiveness of the proposed method.

Facial Region Segmentation Based Emotion Recognition Using K-Nearest Neighbors

The use of human biometrics for automatic identity verification has become widespread. Mostly used human biometrics are face, fingerprint, iris, gait, retina, voice, hand geometry etc. Among them iris is an externally visible, yet protected organ whose unique epigenetic pattern remains stable throughout one's whole life. These characteristics make it very attractive to use as a biometric for identifying individuals. This paper presents a detailed study of iris recognition technique. It encompasses an analysis of the reliability and the accuracy of iris as a biometric of person identification. The main phases of iris recognition are segmentation, normalization, feature encoding and matching. In this work automatic segmentation is performed using circular Hough transform method. Daugman's rubber sheet model is used in normalization process. Four level phase quantization based 1D Log-Gabor filters are used to encode the unique features of iris into binary template. And finally the Hamming distance is considered to examine the affinity of two templates in matching stage. We have experimented a better recognition result for CASIA-iris-v4 database.

Human iris as a biometric for identity verification

This research proposed a multi-modal person authentication system developed by feature level fusion of iris recognition and retina recognition. The reasons for choosing iris and retina as biometric characteristics are they provide the highest level of uniqueness, performance, universality, and circumvention. The ‘curse-of-dimensionality’ problem introduced in feature level fusion which was the main limitation of the prior works in this field, was minimized to a great extent by applying Principal Component Analysis (PCA) on the augmented feature template. To validate this approach, iris and retina images obtained from ‘IITD’ and ‘DRIVE’ datasets respectively are used. The recognition rate for the proposed multi-modal biometric system was 98.37% whereas it is 96.74% and 94.56% for iris recognition and retina recognition respectively.

An Expert Multi-Modal Person Authentication System Based on Feature Level Fusion of Iris and Retina Recognition

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Barshon Sen

Author Tools

Chat about Author

Papers

Human iris as a biometric for identity verification

An Expert Multi-Modal Person Authentication System Based on Feature Level Fusion of Iris and Retina Recognition