An Improved Iris Recognition Algorithm Based on Hybrid Feature and ELM

Wang, Juan

doi:10.1088/1757-899x/322/5/052030

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…Using datasets of CASIA V-1 and CASIA V-4 experiments are carried out by the researcher. Results of the proposed work indicate ELM can learn faster due to the 16 fps speed [2].…”

Section: Literature Surveymentioning

confidence: 95%

An investigation of IRIS Recognition Techniques: A Literature Survey

Divya¹,

Rajendra²

2023

ACE

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Iris is a colored muscle located within the eye that helps to regulate the amount of light that reaches the eye. It has some specific textural details that is not easily altered or manipulated, making it a characteristic that is ideally suited to biometric systems. Iris has been considered in the field of biometric applications for authentication purpose. The main purpose of this study is, due to the stability and unique feature available in Iris region. Iris patterns play a major role in many possible identification or authentication applications due to their uniqueness, durability and stability, universality. Iris recognition techniques have evolved tremendously in biometric identification and authentication systems over the past two decades, since their evolution. The technique of Iris recognition is quantifiable, robust, and highly accurate so that it achieves the fundamental tenant of ideal biometric technology. Purpose of this paper is to include a timeline analysis of different iris recognition techniques and the development of a novel algorithm for the iris recognition system. This paper also discusses various methods used in performing the iris recognition measures involved.

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“…Using datasets of CASIA V-1 and CASIA V-4 experiments are carried out by the researcher. Results of the proposed work indicate ELM can learn faster due to the 16 fps speed [2].…”

Section: Literature Surveymentioning

confidence: 95%

An investigation of IRIS Recognition Techniques: A Literature Survey

Divya¹,

Rajendra²

2023

ACE

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“…However the model is utilized only one strategy to protect the template which affects the recognition rate and needs to be improved using different combinations of feature extraction techniques and classifiers. Juan Wang [3] generated multi-granularity hybrid features from the two dimensional Gabor filters and GLCM techniques. The obtained features from both the GF and GLCM are classified using Extreme Learning Machine.…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of MSB Based Iris Recognition Using Hybrid Features Extraction Technique

Harakannanavar¹,

Prashanth²,

Raja³

2019

IJEAT

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In the modern days, biometric identification is more promising and reliable to verify the human identity. Biometric refers to a science for analyzing the human characteristics such as physiological or behavioral patterns. Iris is a physiological trait, which is unique among all the biometric traits to recognize an individual effectively. In this paper, MSB based iris recognition based on Discrete Wavelet Transform, Independent Component Analysis and Binariezed Statistical Image Features is proposed. The left and right region is extracted from eye images using morphological operations. Binary split is performed to divide the eight-bit binary of every pixel into four bit Least Significant Bits and four bit Most Significant Bits. DWT is applied on four bit MSB to extract the iris features. Then ICA is applied on approximate sub band to extract the significant details of iris. The obtained features are then applied on BSIF to obtain the enhanced response with final features. Finally features produced are matched with the test features using Euclidean distance classifier on CASIA database. The experiments are performed on proposed iris model using MATLAB 7.0 software considering various combinations of Person inside Database (PID’s) and Person outside Database (POD’s) to evaluate the recognition accuracy of the proposed iris model.

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“…Image of iris would be subjected to distortions because of improper illumination. Work done by Wang (2018) [13] established enhanced extreme learning machine (ELM) for identification of individual [14] established the innovative protection outline that delivered security to iris images utilizing watermarking along with visual cryptography strategies. Iris images would be secured with the help of watermarking practice while utilization of Discrete Cosine Transform in implanting watermarked scripted image to iris image was within least frequency boundary.…”

Section: Literature Surveymentioning

confidence: 99%

Integrated Discrete Wavelet Transform and Singular Value Decomposition based Biometric Watermarking for Authentication using Iris Images

Joyce*

2019

IJRTE

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Recently with respect to developments in watermarking techniques, intruders are capable of accessing the database. Several strategies are designed for securing information. Utilization of watermarks will be recommended with the background pertaining to several biometric strategies comprising fingerprints, position of palm, gait, iris, speech, etc. Among the available approaches digital watermarking will be one of the effective techniques. Prominent manner of carrying out the digital watermarking will be spatial domain along with strong transform domain. This paper establishes a strategy for providing the watermark, besides biometric information by utilizing Wavelet Transform in addition to Singular Value Decomposition. Utilization of biometric rather than conservative watermark enhances protection of information. The utilized biometric will be iris. Consideration of iris template as watermark guarantees iris pattern communication, whereas watermarking iris images might be involved in assisting, safeguarding of information in addition to identification of iris image moderation. The mentioned work involved in carrying out analysis means checking the authentication under various attacks along with no attacks. Motivation in utilization of watermarking will depend on enhancement in the field of biometric recognition. On the other hand, utilization of biometric patterns as “message” has to be designed with conservative strong watermarking for safeguarding the information. So as to ensure biometric appreciation to the development of watermark.

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An Improved Iris Recognition Algorithm Based on Hybrid Feature and ELM

Cited by 4 publications

References 7 publications

An investigation of IRIS Recognition Techniques: A Literature Survey

An investigation of IRIS Recognition Techniques: A Literature Survey

Performance Analysis of MSB Based Iris Recognition Using Hybrid Features Extraction Technique

Integrated Discrete Wavelet Transform and Singular Value Decomposition based Biometric Watermarking for Authentication using Iris Images

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