A new iris recognition method based on a robust iris segmentation approach is presented in this paper for improving iris recognition performance. The robust iris segmentation approach applies power-law transformations for more accurate detection of the pupil region, which significantly reduces the candidate limbic boundary search space for increasing detection accuracy and efficiency. The limbi...

Problem statement: In any real time biometric system processing speed and recognition time are crucial parameters. Reducing processing time involves many parameters like normalization, FAR, FRR, management of eyelid and eyelash occlusions, size of signature etc. Normalization consumes substantial amount of time of the system. This study contributes for improved iris recognition system with redu...

Iris recognition is regarded as the most stable and accurate biometric identification system. An iris recognition system basically consists of four stepssegmentation, normalization, encoding and matching. This paper proposes an efficient approach for iris segmentation. This segmentation approach uses a modified canny edge detection algorithm by considering gradient finding, nonmaximum suppressi...

A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the individual. Iris recognition is regarded as the most reliable and accurate biometric identification system available. Iris recognition systems capture an image of an individual's eye, the iris in the image is then segmented and normalized for feature extraction proc...

In this paper, a novel and simple iris feature extraction technique is proposed for iris recognition of high performance. We use one dimensional circular ring to represent iris features. The reduced and significant features afterward are extracted by Sobel operator and 1D wavelet transform. So as to improve the accuracy, this paper combines Probabilistic Neural Network (PNN) and Particle Swarm ...

Iris recognition is the most accurate of the top three biometrics: fingerprints, facial recognition, and iris recognition. Iris recognition has a false accept rate of 1 in 1.2 million for one eye (1 in 1.44 trillion for two eyes) regardless of database size [1]. Iris recognition is the easiest one among biometrics related with the eye. It works with simple CCD camera and does not need direct co...

Abstract Traditional iris recognition has high accuracy and low misrecognition rate. However, in the case of mobile terminal or distance, image resolution quality decrease, rate also decreases. To solve above problems, this article is based on deep learning technology, basis single mode state recognition, from different levels multimodal integration, eyes score level fusion research, put forwar...

Iris recognition can offer high-accuracy person identification, particularly when the acquired iris image is well focused. Iris identification is one of the most eye-catching approaches due to its nature of randomness, texture stability over a life time, high entropy density and non-invasive acquisition. While the performance of iris identification on high quality image is well investigated, no...

Iris recognition technique is now regarded among the most trustworthy biometrics tactics. This is basically ascribed to its extraordinary consistency in identifying individuals. Moreover, this technique is highly efficient because of iris’ distinctive characteristics and due to its ability to protect the iris against environmental and aging effects. The Problem statement of this work is that th...

The iris (Fig. 1) is a protected internal organ behind the cornea which gives color to the eye [1]. Ophthalmologists Flom and Safir first noted that the iris is very unique for each person and remains unchanged after the first year of human life [3]. For each person, the left eye is distinctive from the right eye [3]. In 1987, they described a manual approach for iris recognition based on visib...