Iris recognition based on <scp>few‐shot</scp> learning

Lei, Songze; Li, Yonggang; Xiao, Feng; Tian, Feng

doi:10.1002/cav.2018

Cited by 3 publications

(4 citation statements)

References 18 publications

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“…Malgheet [ 33 ] has provided a summary of the many techniques for iris recognition research. Lei [ 2 ] proposed a model-agnostic meta-learning (MAML)-based several-shot learning method (new-shot learning) for iris recognition to solve the problem of a limited number of samples in deep learning for iris recognition technology; Sun [ 25 ] proposed an open-set iris recognition method based on deep learning that can effectively differentiate iris samples of location classes without impacting the known iris recognition capability. Wang [ 34 ] introduced a cross-spectral iris identification algorithm based on convolutional neural networks (CNNs) and supervised discrete hashing, which not only achieves superior performance than previously examined CNN [ 35 ] designs but also greatly reduces the template size.…”

Section: Biometric Recognition Mechanismmentioning

confidence: 99%

“…As a result of the development of biometric identification technology, biological traits provide major benefits, such as passwords, and human biometric characteristics for identity authentication have attracted considerable attention. Biometric identification mainly includes iris recognition [ 1 , 2 , 3 ], facial recognition [ 4 ], voice recognition [ 5 ], retina recognition [ 6 ], palm print recognition [ 7 ], vein recognition [ 8 , 9 ], fingerprint recognition [ 10 , 11 , 12 ], and so on. Biometric technology has vast applicability and business potential.…”

Section: Introductionmentioning

confidence: 99%

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A Review of Fingerprint Sensors: Mechanism, Characteristics, and Applications

Niu

et al. 2023

Micromachines

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Identification technology based on biometrics is a branch of research that employs the unique individual traits of humans to authenticate identity, which is the most secure method of identification based on its exceptional high dependability and stability of human biometrics. Common biometric identifiers include fingerprints, irises, and facial sounds, among others. In the realm of biometric recognition, fingerprint recognition has gained success with its convenient operation and fast identif ication speed. Different fingerprint collecting techniques, which supply fingerprint information for fingerprint identification systems, have attracted a significant deal of interest in authentication technology regarding fingerprint identification systems. This work presents several fingerprint acquisition techniques, such as optical capacitive and ultrasonic, and analyzes acquisition types and structures. In addition, the pros and drawbacks of various sensor types, as well as the limits and benefits of optical, capacitive, and ultrasonic kinds, are discussed. It is the necessary stage for the application of the Internet of Things (IoT).

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Section: Biometric Recognition Mechanismmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Review of Fingerprint Sensors: Mechanism, Characteristics, and Applications

Niu

et al. 2023

Micromachines

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“…Lately, DL 19 and especially CNNs 20 have been widely used in iris recognition because of their robust and accurate predictions. An interactive variant of DL (U-Net CNN) was proposed 21 .…”

Section: Introductionmentioning

confidence: 99%

Performance boosting of image matching-based iris recognition systems using deformable circular hollow kernels and uniform histogram fusion images

2022

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. Identification of people using different biometric data is becoming more important in network society. Biometrics include voice, ears, palms, fingerprints, faces, iris, retina, and hand shapes. Among these features, iris detection gets more attention because each iris type is unique and does not change throughout life. In this study, an iris recognition framework is proposed using deformable circular hollow kernels and uniform histogram fusion images (UHFIs). This system introduces two different approaches for the iris recognition: one with image matching without machine learning and the other one with machine learning. In the first approach, image matching through Gabor features is employed. After getting the circularly cropped UHFI, Gabor features are extracted and employed in the image matching-based recognition system. In addition, a normalized cross-correlation coefficient is used as a similarity metric to compare the Gabor feature vectors. In the second approach, Gabor feature images are extracted and employed in deep learning (DL). According to the experimental results, the proposed system reaches around 89% accuracy on MMU1, 86% accuracy on MMU2, and about 50% accuracy on the CASIAV3 and CASIAV4 datasets when no machine learning is employed. Extensive benchmarking results also indicate that the proposed system boosts the performance of conventional systems by at least around 40% in terms of accuracy in the absence of machine learning. However, in the presence of machine learning, experimental results show that the proposed method with DL achieves an accuracy of up to 100% for MMU1, MMU2, CASIAV3, and CASIAV4 datasets.

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“…Fingerprint unlocking based on capacitance sensing principle [6][7][8][9] is more attractive than other technologies such as face, [10][11][12][13][14] iris, and [15][16][17][18] ultrasonics [19][20][21][22][23] types. Unfortunately, the contacts of fingertip with capacitance sensor module cause ESD damage easily, reducing the sensor lifetime and worsening the whole product reliability.…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of pixel‐based adjustable ESD protection circuits for capacitive fingerprint biometric sensors

Lin

Hsieh

Chang

2022

Circuit Theory & Apps

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The trade-off design between ESD endurability and polymer thickness on sensor plate for capacitive fingerprint is emerging in these years since the image quality of fingerprint is vital for biometrics authentication. In this paper, we propose a pixel-based ESD protection circuitry in parallel to GDPMOS and GGNMOS and an adjustable body terminal in proposed pixel-based ESD circuit to meet and exceed air discharge ±15 kV without thicker polymer. With the proposed pixel-based adjustable ESD protection circuit, the maximum air discharge achieved by (18 kV, À17 kV), and 10 out of 10 samples passed +15 kV, and 10 out of 10 samples passed À15 kV. Compared to conventional ground wall fingerprint sensor, the maximum air discharge was stopped by (15 kV, À15 kV), and just merely five out 10 samples passed 15 kV, seven out of 10 samples passed À15 kV. Besides the excellent ESD endurability, the proposed pixel-based ESD protection circuit achieves air-discharge 15.5 kV and grayscale count 230 at merely 50 μm thickness of polymer. Compared to the conventionally used ground wall sensor types, it passes only 13 kV and a grayscale count of 210 at a polymer thickness of 50 μm, but fails 15 KV Level 4 of IEC-61000-4-2. Even with polymer thickness increased to 250 μm, ground wall sensor types barely passed 15 kV, and the grayscale count dropped to 120, which is far from PIV image standard.

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Iris recognition based on few‐shot learning

Cited by 3 publications

References 18 publications

A Review of Fingerprint Sensors: Mechanism, Characteristics, and Applications

A Review of Fingerprint Sensors: Mechanism, Characteristics, and Applications

Performance boosting of image matching-based iris recognition systems using deformable circular hollow kernels and uniform histogram fusion images

Design and implementation of pixel‐based adjustable ESD protection circuits for capacitive fingerprint biometric sensors

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