Image Processing on Eye Image Using SURFFeature Extraction (original) (raw)

Ocular biometrics: automatic feature extraction from eye images

2011

We presents a general framework for image processing of eye images with a particular view on feature extraction. In eye imaging the process of the diagnosis and feature extraction is one complete system and extracting features from eye images can be used to automated interpretation of the images. We consider for human recognition based on the retinal and the conjunctival vasculature.

Feature extraction for IRIS recognition

Communications in Computer and Information Science, 2009

Iris templates can be matched using various matching techniques; each approach to iris recognition has several aspects being taken into consideration. One of the most important aspect being the presence of a lens in the users eye. The biometric templates, including iris and users attributes which are produced by using different recognition techniques can be matched through the Iris-templates can be replicated. From this we conclude that, without a thorough security analysis, iris templates cannot be assumed safe and secure. In this paper, we are going to discuss about the presence of a contact lens, particularly a colored lens, and the challenges we face as it alters the natural iris patterns. In this paper we will use a feature extraction method to remove the effect of colored contact lenses and make it more secure. This has proven to be very efficient and also less space consuming.

BIOMETRIC OBSERVANCE OF EYE PATTERN USING ALGORITHMS AND DIGITAL IMAG E PROCESSING

In this paper, we describe a non - intrusive vision - based system for the detection of eye pattern of driver fatigue. The system uses a color video camera that points directly towards the driver's face and monitors the driver's eyes in order to de tect micro - sleeps (short periods of sleep).The critical points at which driver fatigue related collisions happen are between 2am to 6 am and midafternoon between 2pm to 4 pm when our "circadian rhythm" or body clock is at its lowest point. If a driver persists in fighting sleep while driving the impairment level is the same as driving while over the drink drive limit. Eventually a driver will drift in and out of consciousness and experience "micro sleeps" which can last for up to 10 seconds. In this time a drive r has no control of the vehicle. Drivers can experience such a micro sleep with their eyes wide open. Biometric Observance of Eye Pattern Using Algorithms and Digital Image Processing, has been developed, using a non - intrusive machine vision based concepts . The system uses the webcam or digital camera that points directly towards the driver’s face and monitors the driver’s eyes in order to detect fatigue. In such a case when fatigue is detected, a warning signal is issued to alert the driver. This paperdesc ribes how to find the eyes, and also how to determine if the eyes are open or closed. The system deals with using information obtained for the face cropped version of the image to find the edges of the face, which narrows the area of wherethe eyes may exis t. Once the face areais found, the eyes are found by computing the circular Hough Transform. The circular Hough Transform detects the circles in the image and hence encircles the iris. In this way, the iris of the driver is continuouslymonitored and tracke d. If the circle is not detected for any fixed number of consecutive frames, then eyes are assumed to be closed. In this condition, the system draws the conclusion that the driver is falling asleep and issues a warning signal. The international statistics shows that a large number of road accidents are caused by driver fatigue. Therefore, a system that can detect oncoming driver fatigue and issue timely warning could help in preventing many accidents, and consequently save money and reduce personal sufferin g..can determine whether the eyes are open or closed. If the eyes are found closed for 5 consecutive frames, the system draws the conclusion that the driver is falling asleep and issues a warning signal. The algorithm is proposed, implemented, tested, and found working satisfactorily.

Personal verification system based on retina and SURF descriptors

2016 13th International Multi-Conference on Systems, Signals & Devices (SSD), 2016

Today, Human recognition, especially based on retina, has been an important and attractive topic of scientific research. Most efforts in Biometrics tend to develop more efficient systems which compromise speed and robustness of authentication. In fact, retinal images often suffer from imperfections such as background intensity variation, affine transformations (translation, rotation, scale changes, etc.) variations from pattern to other. These defects can seriously affect features extraction in terms of quality and execution time. In this context, in order to overcome these defects, we propose in this paper a novel retinal verification system based on the Speeded Up Robust Features (SURF) extraction. This feature extraction method is so fast and invariant to the affine transformations such as rotation, scale changes and translation. We employ the Optical Disc interest Ring (ODR) method as a preprocessing step in order to further speed up the system and improve the performance. A sub...

Design and Implementation Iris Recognition System Using Texture Analysis

Al-Nahrain Journal for Engineering Sciences, 2013

The aim of this work is to produces a technology of recognition and identifies of the person by using the iris. The work was started by reading the images of eyes (UBIRIS database). After that, the iris region localized from the eye image by using the method of image processing. The iris shape is circular so it transfer to rectangular shape and enhance the image and remove the noise like eyelashes and flash of the camera. Then the image quantized from 256 grey levels to 16 grey levels. Four statistical functions used because these functions give us accurate description of iris, the samples had been taken in four angles. The information for each sample is save in database. The last stage is to classify the samples by using neural network. The results will prove that the work have high accurate conclusions.

PERFORMANCE ANALYSIS OF FEATURES EXTRACTION ON IRIS RECOGNITION SYSTEM

IJCIRAS, 2020

One of the most reliable biometric advancements is iris recognition system. The performance of an iris recognition system can be determined by the feature extraction time and recognition rate. The selection of the feature subset and the classification has become an important issue in the field of iris recognition. In the proposed system, it implements the iris recognition system using a new correlation method based on entropy and skewness. Here, the system uses the eye image from the CASIA Iris Database version 4.0 instead of scanning from the eye camera real time. After that the system removes the noise from the eye image using median filtering. Then the system detects the edge using Sobel edge detection. And the system performs the segmentation with the degree of 36. So the segments are a total of 10. Finally the system extracts the features using the correlation method based on entropy and skewness. Then the system evaluates the performance of the iris features extraction compared with other traditional iris feature extraction system.

A Novel Method for User Authentication on Cloud Computing Using Face Recognition System

Integrated Intelligent Research, 2012

Face Recognition is a vital role in the field of computer Science and Engineering. Face recognition presents a challenging problem in the field of image processing and computer vision, and as such has received a great deal of attention over the last few years because of its many applications in various domains. A lot of algorithms and techniques have been proposed for solving authenticated a person and face recognition system. Social Networking has become today's lifestyle and anyone can easily receive information about everyone in the world. It is very useful if a personal identity can be obtained from the any device and also connected to social networking. Cloud computing is a new technology in the IT industry. In that, identifying authorized user is a major problem. The user wanting to access the data or services needs to be registered and before every access to data or services; his/her identity must be authenticated for authorization. There are several authentication techniques including traditional and biometrics but it has some drawbacks. In this paper, we have proposed new face recognition system (FRS) which overcome all drawbacks of traditional and other biometric authentication techniques and enables only authorized users to access data or services from cloud server.

Authentication System For IRIS Biometric Recognition Using Texture Analysis

International Journal of Modern Trends in Engineering & Research, 2017

A biometric system provides automatic identification of an individual based on a unique feature possessed by the individual. Iris recognition has emerged as one of the most preferred biometric modalities for automated personal identification. Iris is an internally protected organ whose texture is stable from birth to death, as its texture is unique in each individual, so it is reliable and accurate method of biometric technology. Our new approach for iris recognition has 4 stages: image preprocessing, pupil detection, hybrid feature extraction and classification. In feature extraction, texture analysis is done, which refers to the characterization of regions in an image by their texture content. It attempts to quantify intuitive qualities described by terms such as rough, smooth, silky, or bumpy as a function of the spatial variation in pixel intensities. In classification, template will be compared to a stored template in a database and then it will determine authenticated user.

Feature Extraction Methods for IRIS Recognition System: A Survey

International Journal of Computer Science and Information Technology, 2022

Protection has become one of the biggest fields of study for several years, however the demand for this is growing exponentially mostly with rise in sensitive data. The quality of the research can differ slightly from any workstation to cloud, and though protection must be incredibly important all over. Throughout the past two decades, sufficient focus has been given to substantiation along with validation in the technology model. Identifying a legal person is increasingly become the difficult activity with the progression of time. Some attempts are introduced in that same respect, in particular by utilizing human movements such as fingerprints, facial recognition, palm scanning, retinal identification, DNA checking, breathing, speech checker, and so on. A number of methods for effective iris detection have indeed been suggested and researched. A general overview of current and state-of-the-art approaches to iris recognition is presented in this paper. In addition, significant advan...

An Effective Feature Extraction Approach for Iris Recognition System

Indian journal of science and technology, 2016

Background/Objectives: Iris recognition system refers to a system used for identifying different iris texture patterns for different applications. The research is aimed at developing a system that improves the efficiency of the iris recognition system and make it more reliable and robust. Methods: To this end, we have developed a system based on compound local binary pattern technique. Compound local binary technique is a spatial domain technique, which assign a 2P bit code to central pixel based on the local neighbourhood comprising of P neighbours. The operator takes into consideration both the sign and magnitude information of the central and corresponding neighbour grey values. The unique and abundant features extracted through Compound Local Binary Pattern (CLBP) operator act as input to the neural network classifier. Findings: The system has been tested over 50 eye images taken from CASIA database. Iris recognition system based on Compound local binary pattern technique along with neural network used as classifier improves the accuracy of system in comparison to existing feature extraction approaches. In this proposed research, recognition rate achieved is 96%.