Multimodal Biometrics Using Fingerprint, Palmprint, and Iris With a Combined Fusion Approach

Sanjekar, Priti S.; Patil, J. B.

doi:10.4018/ijcvip.2019100101

Cited by 4 publications

(2 citation statements)

References 31 publications

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“…According to the authors, a 95.23% accuracy was attained, which is higher than previous methods at the time. Sanjekar and Patil [20] also recommended employing a fingerprint, palm-print, and iris to validate a person's identity. To extract features, the wavelet transfer method was utilized.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-modal palm-print and hand-vein biometric recognition at sensor level fusion

AlMahafzah

Abukhalil

Alksasbeh

et al. 2023

IJECE

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When it is important to authenticate a person based on his or her biometric qualities, most systems use a single modality (e.g. fingerprint or palm print) for further analysis at higher levels. Rather than using higher levels, this research recommends using two biometric features at the sensor level. The Log-Gabor filter is used to extract features and, as a result, recognize the pattern, because the data acquired from images is sampled at various spacing. Using the two fused modalities, the suggested system attained greater accuracy. Principal component analysis was performed to reduce the dimensionality of the data. To get the optimum performance between the two classifiers, fusion was performed at the sensor level utilizing different classifiers, including <em>k</em>-nearest neighbors and support vector machines. The technology collects palm prints and veins from sensors and combines them into consolidated images that take up less disk space. The amount of memory needed to store such photos has been lowered. The amount of memory is determined by the number of modalities fused.

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Section: Literature Reviewmentioning

confidence: 99%

Multi-modal palm-print and hand-vein biometric recognition at sensor level fusion

AlMahafzah

Abukhalil

Alksasbeh

et al. 2023

IJECE

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“…2 In real-world applications, biometric systems used are unimodal. 4 These unimodal biometric systems rely on evidence from a single data source for personal authentication. Though these unimodal biometric systems have numerous advantages, they must contend with several issues, including:…”

Section: Introductionmentioning

confidence: 99%

Cancelable scheme for bimodal biometric authentication

et al. 2023

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.The use of a biometric authentication system (BAS) for reliable automatic human recognition has increased exponentially over traditional authentication systems in recent years. Multimodal BAS was introduced to solve unimodal BAS’s difficulties and improve security. Privacy and security are two significant concerns to be addressed in BAS, as biometric traits are irrevocable. Researchers employed cancelable biometrics in the past few years to propose several privacy-preserving BAS. We propose a privacy-preserving bimodal cancelable BAS (PPBCBAS) to overcome these problems. The traits used in our method are iris and fingerprint. Features are extracted from both the traits, and feature level fusion is done by concatenating the feature vectors of iris and fingerprint. PPBCBAS uses a quotient filter to generate the cancelable template, and the comparison is made on these transformed templates using the modified Hamming distance. PPBCBAS has been tested on three publicly available databases to analyze its efficiency. PPBCBAS satisfies the diversity, irreversibility, and revocability properties and achieves decent performance.

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IoT-based multimodal liveness detection using the fusion of ECG and fingerprint

Gona

Subramoniam²

2022

IJPCC

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Purpose Biometric scans using fingerprints are widely used for security purposes. Eventually, for authentication purposes, fingerprint scans are not very reliable because they can be faked by obtaining a sample of the fingerprint of the person. There are a few spoof detection techniques available to reduce the incidence of spoofing of the biometric system. Among them, the most commonly used is the binary classification technique that detects real or fake fingerprints based on the fingerprint samples provided during training. However, this technique fails when it is provided with samples formed using other spoofing techniques that are different from the spoofing techniques covered in the training samples. This paper aims to improve the liveness detection accuracy by fusing electrocardiogram (ECG) and fingerprint. Design/methodology/approach In this paper, to avoid this limitation, an efficient liveness detection algorithm is developed using the fusion of ECG signals captured from the fingertips and fingerprint data in Internet of Things (IoT) environment. The ECG signal will ensure the detection of real fingerprint samples from fake ones. Findings Single model fingerprint methods have some disadvantages, such as noisy data and position of the fingerprint. To overcome this, fusion of both ECG and fingerprint is done so that the combined data improves the detection accuracy. Originality/value System security is improved in this approach, and the fingerprint recognition rate is also improved. IoT-based approach is used in this work to reduce the computation burden of data processing systems.

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Multimodal Biometrics Using Fingerprint, Palmprint, and Iris With a Combined Fusion Approach

Cited by 4 publications

References 31 publications

Multi-modal palm-print and hand-vein biometric recognition at sensor level fusion

Multi-modal palm-print and hand-vein biometric recognition at sensor level fusion

Cancelable scheme for bimodal biometric authentication

IoT-based multimodal liveness detection using the fusion of ECG and fingerprint

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