Iris recognition by a rotation spreading neural network

Takano, Hironobu; Murakami, Makoto; Nakamura, Keisuke

doi:10.1109/ijcnn.2004.1381054

Cited by 1 publication

(3 citation statements)

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“…In our conducted experiment, N g 5108635324. The FAR is defined as the ratio of the number of impostors being accepted 15 Fig. 17.…”

Section: Performance Evaluation Of Iris Recognitionmentioning

confidence: 99%

“…[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] Those iris recognition approaches can be divided into four categories according to various iris features: 1 phase-based method, 2,3 zero-crossing representation, 4 texture analysis [5][6][7][8][9] and local intensity variation. 1,10,11 A comprehensive review of image understanding for iris biometrics can be referred to the survey paper.…”

Section: Introductionmentioning

confidence: 99%

“…However, the equal error operating point for this system using the given CASIA database was 14%. Takano et al 15 used iris images as the input patterns of the rotation spreading neural network (R-SAN net) to develop an iris recognition system. The proposed method also extracts features directly from iris images rather than adopting the complicated transform.…”

Section: Introductionmentioning

confidence: 99%

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Fast and robust iris recognition

Wan-Tao

Liu

2009

The Imaging Science Journal

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Iris recognition is a reliable biometric identification technology. In this study, the one-to-many identification and one-to-one verification using iris recognition are both examined. Our method consists of three phases: pupil and iris location, iris feature image extraction and iris matching. The CASIA iris database was used in this study to evaluate the performance of the proposed method. The database includes 108 iris classes, each including seven images. The identification accuracy of our method is 99.646%; while the proposed method can achieve an equal error rate of 1.085%. On the other hand, feature extraction of an iris image in our method is only 16.78 ms. Each verification test of an iris images takes 1.4 ms and each identification test over 432 database images takes 151.2 ms. Note the experimental accuracies were reported without manual rejection of poor-quality images or invalid iris-location images, thus the efficiency and robustness of the proposed method can be proven.

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“…In our conducted experiment, N g 5108635324. The FAR is defined as the ratio of the number of impostors being accepted 15 Fig. 17.…”

Section: Performance Evaluation Of Iris Recognitionmentioning

confidence: 99%