Optimal Generation of Iris Codes for Iris Recognition

Hu, Yang; Sirlantzis, Konstantinos; Howells, Gareth

doi:10.1109/tifs.2016.2606083

Cited by 35 publications

(18 citation statements)

References 39 publications

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“…Traditionally, these systems recognize an individual using either a token-based method (such as a key or passwords) or biometric methods (that use the individual’s physical characteristics such as the face [7,8], finger-vein [9], fingerprint [10], or iris patterns [11,12] for recognition). Even though biometric features have proven to be more sufficient in recognizing persons in security systems because of biometric patterns’ advantages of being hard to steal and hard to fake [13], these kinds of biometric features require the cooperation of users and a short capturing distance (z-distance) between camera and user during the image acquisition stage.…”

Section: Introductionmentioning

confidence: 99%

Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras

Nguyen

Hong

Kim

et al. 2017

Sensors

395

159

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The human body contains identity information that can be used for the person recognition (verification/recognition) problem. In this paper, we propose a person recognition method using the information extracted from body images. Our research is novel in the following three ways compared to previous studies. First, we use the images of human body for recognizing individuals. To overcome the limitations of previous studies on body-based person recognition that use only visible light images for recognition, we use human body images captured by two different kinds of camera, including a visible light camera and a thermal camera. The use of two different kinds of body image helps us to reduce the effects of noise, background, and variation in the appearance of a human body. Second, we apply a state-of-the art method, called convolutional neural network (CNN) among various available methods, for image features extraction in order to overcome the limitations of traditional hand-designed image feature extraction methods. Finally, with the extracted image features from body images, the recognition task is performed by measuring the distance between the input and enrolled samples. The experimental results show that the proposed method is efficient for enhancing recognition accuracy compared to systems that use only visible light or thermal images of the human body.

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Section: Introductionmentioning

confidence: 99%

Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras

Nguyen

Hong

Kim

et al. 2017

Sensors

395

159

View full text Add to dashboard Cite

show abstract

“…Body structure, inherent weaknesses, and levels of health and transition are uncovered by the iris, as it changes happen in the one's body as the way they live the life. The eyes are the window of the spirit as said in the past and they are the foremost part of our body which is very necessary of view the beauty of the nature [10]. Therefore, it is key window to one's health.…”

Section: Iris Recognitionmentioning

confidence: 99%

Novel Technique for Iris Detection in Image Processing

.¹

2018

IJRET

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An automated method utilized for biometric identification which includes various mathematical pattern-recognition methods in it is known as the iris recognition method. The videos of the irises of various individual's eyes are studied in this technique. The complex random patterns present within this approach are single, constant moreover can as well viewed from a particular distance. In the base paper, Circular-Hough Transformation is applied with canny edge detection. The GLCM algorithm is applied which will extract the contrast, energy, entropy and heterogeneity of the detected iris has been calculated. To increase the accuracy of iris detection and reduce execution time, improvement in existing GLCM algorithm, feature extraction technique is being proposed. The proposed improvement will be based on applying structural tensor algorithm and improved GLCM for contrast detection.

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“…This method is able to capture different crypts in different locations and able to match topological changes in the detection of the same crypt in different acquisitions. Yang Hu et al, [13] introduced a method to generate optimal iris code for iris recognition. Addition to the binarized code effective iris codes are obtained by adding terms to the objective functions to the optimization problem.…”

Section: Literature Surveymentioning

confidence: 99%

“…. +x O + 1 (13) Where, k is the norm-factor x 1 , x 2 … x n are the sum gradient magnitude of each block. Here X n =36 Figure 8 shows the block normalized histogram of complete image having 105 blocks, so we have total of 3780 normalized value of gradient magnitude.…”

Section: Histogram Of Gradients (Hog)mentioning

confidence: 99%