Efficient Iris Recognition Based on Optimal Subfeature Selection and Weighted Subregion Fusion

Chen, Ying; Liu, Yuanning; Zhu, Xiaodong; He, Fei; Wang, Hongye; Deng, Ning

doi:10.1155/2014/157173

Cited by 17 publications

(8 citation statements)

References 42 publications

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“…Although in the proposed method we are using the UBIRIS dataset, which is a larger dataset in term of images than the CASIA.v1 database used in Park and Park [47], even then the EER value of the proposed method increased by 6.88%. Similarly, the proposed method was compared with the methods of Ying et al [39], Roy et al [40], Zhang et al [53] and He et al [54], which use the CASIA.v3 dataset, which is acquired at close distance. Our proposed method outperformed these methods and achieved higher recognition accuracy by 3.18%, 10.18% , 18%, and 6%, respectively.…”

Section: Comparison With Other State-of-the-art Methodsmentioning

confidence: 99%

“…12. [39], and % from Sazonova et al [55] and are converted into the required form to compare with the proposed method. Finally, we measured the processing time for our proposed method on a desktop PC with a 3.40 GHz Intel Core™ i7 processor and 8 GB of RAM.…”

Section: Comparison With Other State-of-the-art Methodsmentioning

confidence: 99%

“…Tan et al [38] • In this paper, iris image is coarsly segmented using random walker algorithm while in our paper circular edge detector adopted from [15] [39] • For segemntation they used detection of Scale-Space Extreme, Key points Localization and keypoint descriptor representation, while in our method circular edge detection used for segmentation. • The method is based on optimal sub-feature selection strategies and weighted sub-region matching is able to overcome the drawback of filter approach, but this method is not suitable in case of non-ideal iris images.…”

Section: Related Workmentioning

confidence: 99%

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Iris Image Enhancement for the Recognition of Non-ideal Iris Images

Sajjad¹,

Ahn²,

Jung³

2016

KSII TIIS

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Iris recognition for biometric personnel identification has gained much interest owing to the increasing concern with security today. The image quality plays a major role in the performance of iris recognition systems. When capturing an iris image under uncontrolled conditions and dealing with non-cooperative people, the chance of getting non-ideal images is very high owing to poor focus, off-angle, noise, motion blur, occlusion of eyelashes and eyelids, and wearing glasses. In order to improve the accuracy of iris recognition while dealing with non-ideal iris images, we propose a novel algorithm that improves the quality of degraded iris images. First, the iris image is localized properly to obtain accurate iris boundary detection, and then the iris image is normalized to obtain a fixed size. Second, the valid region (iris region) is extracted from the segmented iris image to obtain only the iris region. Third, to get a well-distributed texture image, bilinear interpolation is used on the segmented valid iris gray image. Using contrast-limited adaptive histogram equalization (CLAHE) enhances the low contrast of the resulting interpolated image. The results of CLAHE are further improved by stretching the maximum and minimum values to 0-255 by using histogram-stretching technique. The gray texture information is extracted by 1D Gabor filters while the Hamming distance technique is chosen as a metric for recognition. The NICE-II training dataset taken from UBRIS.v2 was used for the experiment. Results of the proposed method outperformed other methods in terms of equal error rate (EER).

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Section: Comparison With Other State-of-the-art Methodsmentioning

confidence: 99%

Section: Comparison With Other State-of-the-art Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Iris Image Enhancement for the Recognition of Non-ideal Iris Images

Sajjad¹,

Ahn²,

Jung³

2016

KSII TIIS

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“…However, the model has some shortcomings which unable to identify the images corresponding to the classification system and leads difficult to categorize recognition rates. Ying et al, [18] described SIFT to extract the significant features of iris. In order to select the discriminative features, the strategies based on OPDF and MPDF is employed to reduce the redundant feature key points and to reduce the dimensionality of feature element respectively.…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Iris Recognition Using Multi Stage Wavelet Transform Decomposition and Bicubic Interpolation Technique

Harakannanavar¹,

Prashanth²,

Raja³

2019

IJEAT

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Biometric identification is highly reliable for human identification. Biometric is a field of science used for analyzing the physiological or behavioural characteristics of human. Iris features are unique, stable and can be visible from longer distances. It uses mathematical pattern-recognition techniques on video images of one or both iris of an individual's. Compared to other biometric traits, iris is more challenging and highly secured tool to identify the individual. In this paper iris recognition based on the combination of Discrete Wavelet Transform (DWT), Inverse Discrete Wavelet Transform (IDWT), Independent Component Analysis (ICA) and Binariezed Statistical Image Features (BSIF) are adopted to generate the hybrid iris features. The first level and second level DWT are employed in order to extract the more unique features of the iris images. The concept of bicubic interpolation is applied on high frequency coefficients generated by first level decomposition of DWT to produce new set of sub-bands. The approximation band generated by second level decomposition of DWT and new set of sub-bands produced by second level decomposition of DWT are applied on IDWT to reconstruct the coefficients. The ICA 5x5 filters and BSIF are adopted for selecting the appropriate images to extract the final features. Finally based on the matching score between the database image and test image the genuine and imposters are identified. Using CASIA database, training and testing of the features is performed and performance is evaluated considering different combinations of Person inside Database (PID) and Person outside Database (POD).

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“…In addition, in 2014, Y. Chen et al [19] proposed an improved iris recognition system using three feature selection strategies (the orientation probability distribution function, the magnitude probability distribution function, and a compounded strategy combining the two methods for further selection of optimal subfeatures). A matching method based on weighted subregion matching fusion was applied utilizing particle swarm optimization to accelerate the weight determination of different subregions and to match their scores and generate the final decision.…”

Section: Introductionmentioning

confidence: 99%

Efficient Unconstrained Iris Recognition System Based on CCT‐Like Mask Filter Bank

Fathee

Uçan

Abdul–Jabbar

et al. 2019

Mathematical Problems in Engineering

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In this paper, a new personal identification method based on unconstrained iris recognition is presented. We apply a nontraditional step for feature extraction where a new circular contourlet filter bank is used to capture the iris characteristics. This idea is based on a new geometrical image transform called the circular contourlet transform (CCT). An efficient multilevel and multidirectional contourlet decomposition method is needed to form a reduced-length quantized feature vector with improved performance. The CCT transform provides both multiscale and multioriented analysis of iris features. Circular contourlet-like mask filters can be used with shapes just like the 2D circular-support regions in different scales and directions. A reduced recognition system is realized using a single branch of the whole decomposition bank, highlighting a system realization with lower complexity and fewer computations. In the proposed recognition system, only five out of seven elements of the gray level cooccurrence matrix are required in the creation of the feature vector, which leads to a further reduction in computations. In addition, the highly discriminative frequency regions due to the use of circular-support decompositions can result in highly accurate feature vectors, reflecting good recognition rates for the proposed system. It is shown that the proposed system has encouraging performance in terms of high recognition rates and a reduced number of elements of the feature vector. This reflects reliable and rapid recognition properties. In addition, some promising characteristics of the system are apparent since it can efficiently be realized with lower computation complexity.

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Efficient Iris Recognition Based on Optimal Subfeature Selection and Weighted Subregion Fusion

Cited by 17 publications

References 42 publications

Iris Image Enhancement for the Recognition of Non-ideal Iris Images

Iris Image Enhancement for the Recognition of Non-ideal Iris Images

Performance Analysis of Iris Recognition Using Multi Stage Wavelet Transform Decomposition and Bicubic Interpolation Technique

Efficient Unconstrained Iris Recognition System Based on CCT‐Like Mask Filter Bank

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