28 research outputs found
Comparative Analysis of Iris Segmentation and Iris Feature Extraction Techniques
Iris recognition is identification and verification of an individual based on their respective unique iris patterns. This system is preferred because it is stable: Iris of an individual does not change by the passing of time; Unique: Each person has a different Iris pattern; Flexible: it can easily be incorporated into security systems; Reliable: No theft because people can�t create an iris of another person;In our survey project the processes of Iris segmentation and Feature Extraction have been studied in depth.In this survey paper the various techniques that are used in Iris segmentation and Feature extraction processes are compared and analysed and a conclusion is drawn from them
Feature extraction using two dimensional (2D) legendre wavelet filter for partial iris recognition
An increasing need for biometrics recognition systems has grown substantially to
address the issues of recognition and identification, especially in highly dense areas
such as airports, train stations, and financial transactions. Evidence of these can be
seen in some airports and also the implementation of these technologies in our mobile
phones. Among the most popular biometric technologies include facial, fingerprints,
and iris recognition. The iris recognition is considered by many researchers to be the
most accurate and reliable form of biometric recognition because iris can neither be
surgically operated with a chance of losing slight nor change due to aging. However,
presently most iris recognition systems available can only recognize iris image with
frontal-looking and high-quality images. Angular image and partially capture image
cannot be authenticated with the existing method of iris recognition. This research
investigates the possibility of developing a technique for recognition partially captured
iris image. The technique is designed to process the iris image at 50%, 25%, 16.5%,
and 12.5% and to find a threshold for a minimum amount of iris region required to
authenticate the individual. The research also developed and implemented two
Dimensional (2D) Legendre wavelet filter for the iris feature extraction. The Legendre
wavelet filter is to enhance the feature extraction technique. Selected iris images from
CASIA, UBIRIS, and MMU database were used to test the accuracy of the introduced
technique. The technique was able to produce recognition accuracy between 70 – 90%
CASIA-interval with 92.25% accuracy, CASIA-distance with 86.25%, UBIRIS with
74.95%, and MMU with 94.45%
Robust Iris Segmentation Based on Fully Convolutional Networks and Generative Adversarial Networks
The iris can be considered as one of the most important biometric traits due
to its high degree of uniqueness. Iris-based biometrics applications depend
mainly on the iris segmentation whose suitability is not robust for different
environments such as near-infrared (NIR) and visible (VIS) ones. In this paper,
two approaches for robust iris segmentation based on Fully Convolutional
Networks (FCNs) and Generative Adversarial Networks (GANs) are described.
Similar to a common convolutional network, but without the fully connected
layers (i.e., the classification layers), an FCN employs at its end a
combination of pooling layers from different convolutional layers. Based on the
game theory, a GAN is designed as two networks competing with each other to
generate the best segmentation. The proposed segmentation networks achieved
promising results in all evaluated datasets (i.e., BioSec, CasiaI3, CasiaT4,
IITD-1) of NIR images and (NICE.I, CrEye-Iris and MICHE-I) of VIS images in
both non-cooperative and cooperative domains, outperforming the baselines
techniques which are the best ones found so far in the literature, i.e., a new
state of the art for these datasets. Furthermore, we manually labeled 2,431
images from CasiaT4, CrEye-Iris and MICHE-I datasets, making the masks
available for research purposes.Comment: Accepted for presentation at the Conference on Graphics, Patterns and
Images (SIBGRAPI) 201
IRDO: Iris Recognition by Fusion of DTCWT and OLBP
Iris Biometric is a physiological trait of human beings. In this paper, we propose Iris an Recognition using Fusion of Dual Tree Complex Wavelet Transform (DTCWT) and Over Lapping Local Binary Pattern (OLBP) Features. An eye is preprocessed to extract the iris part and obtain the Region of Interest (ROI) area from an iris. The complex wavelet features are extracted for region from the Iris DTCWT. OLBP is further applied on ROI to generate features of magnitude coefficients. The resultant features are generated by fusing DTCWT and OLBP using arithmetic addition. The Euclidean Distance (ED) is used to compare test iris with database iris features to identify a person. It is observed that the values of Total Success Rate (TSR) and Equal Error Rate (EER) are better in the case of proposed IRDO compared to the state-of-the art technique
Accurate Detection of Non-Iris Occlusions
Abstract-Accurate detection of iris eyelids and reflections is the prerequisite for the accurate iris recognition, both in near-infrared or visible spectrum measurements. Undected iris occlusions otherwise dramatically decrease the iris recognition rate. This paper presents a fast multispectral iris occlusions detection method based on the underlying multispectral spatial probabilistic iris textural model and adaptive thresholding. The model adaptively learns its parameters on the iris texture part and subsequently checks for iris reflections, eyelashes, and eyelids using the recursive prediction analysis. Our method obtains better accuracy with respect to the previously performed Noisy Iris Challenge Evaluation contest. It ranked first from the 97+2 alternative methods on this large colour iris database
Adaptive fuzzy switching noise reduction filter for iris pattern recognition
Noise reduction is a necessary procedure for the iris recognition systems. This paper proposes an adaptive fuzzy switching noise reduction (AFSNR) filter to reduce noise for iris pattern recognition. The proposed low complexity AFSNR filter removes noise pixels by fuzzy switching between an adaptive median filter and the filling method. The threshold values of AFSNR filter are calculated on the basis of the histogram statistics of eyelashes, pupils, eyelids, and light illumination. The experimental results on the CASIA V3.0 iris database, with genuine acceptance rate equals 99.72%, show the success of the proposed method
BIRD: Watershed Based IRis Detection for mobile devices
Communications with a central iris database system using common wireless technologies, such as tablets and smartphones, and iris acquisition out of the field are important functionalities and capabilities of a mobile iris identification device. However, when images are acquired by means of mobile devices under uncontrolled acquisition conditions, noisy images are produced and the effectiveness of the iris recognition system is significantly conditioned. This paper proposes a technique based on watershed transform for iris detection in noisy images captured by mobile devices. The method exploits the information related to limbus to segment the periocular region and merges its score with the iris' one to achieve greater accuracy in the recognition phase