Feature Component-Based Extreme Learning Machines for Finger Vein Recognition

Xie, Shan Juan; Yoon, Sook; Ju, Yang; Lu, Yu; Park, Dong Sun; Zhou, Bin

doi:10.1007/s12559-014-9254-3

Cited by 39 publications

(11 citation statements)

References 31 publications

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“…However, conventional finger vein approaches employ distance-based methods during the matching stage. Accuracy rate of almost all the proposed machine learning finger vein algorithms is close to 100% [61,85,86,105,115]. Table 6 lists the existing literature on traditional machine learning techniques-related finger vein recognition.…”

Section: Traditional Machine Learning Finger Vein Recognition Methodsmentioning

confidence: 84%

A Systematic Review of Finger Vein Recognition Techniques

Han-gang²,

et al. 2018

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Biometric identification is the study of physiological and behavioral attributes of an individual to overcome security problems. Finger vein recognition is a biometric technique used to analyze finger vein patterns of persons for proper authentication. This paper presents a detailed review on finger vein recognition algorithms. Such tools include image acquisition, preprocessing, feature extraction and matching methods to extract and analyze object patterns. In addition, we list some novel findings after the critical comparative analysis of the highlighted techniques. The comparative studies indicate that the accuracy of finger vein identification methods is up to the mark.

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Section: Traditional Machine Learning Finger Vein Recognition Methodsmentioning

confidence: 84%

A Systematic Review of Finger Vein Recognition Techniques

Han-gang²,

et al. 2018

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“…Note that N is different for datasets, e.g., according to Moreover, all parameters were optimized based on a validation set of [33][34][35][36][37] cases which were selected in the respective test period (illustrated in Table 2). In the test period, one case was selected in every 10 days to construct the validation dataset and the remaining cases were employed as the test data.…”

Section: Setup Of Re-elm Os-elm and Mcos-elmmentioning

confidence: 99%

Imbalanced Learning for Air Pollution by Meta-Cognitive Online Sequential Extreme Learning Machine

Vong

Chiu

et al. 2014

Cogn Comput

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Many time series problems such as air pollution index forecast require online sequential learning rather than batch learning. One of the major obstacles for air pollution index forecast is the data imbalance problem so that forecast model biases to the majority class. This paper proposes a new method called meta-cognitive online sequential extreme learning machine (MCOS-ELM) that aims to alleviate data imbalance problem and sequential learning at the same time. Under a real application of air pollution index forecast, the proposed MCOS-ELM was compared with retrained ELM and online sequential extreme learning machine in terms of accuracy and computational time. Experimental results show that MCOS-ELM has the highest efficiency and best accuracy for predicting the minority class (i.e., the most important but with fewest training samples) of air pollution level.

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“…This makes finger vein media resistant to theft and forgery. In practice, however, FVRSs suffer from external factors such as imaging models [ 10 ] and uneven illumination [ 11 , 12 ], and internal factors including scattering [ 13 ] and finger tissue [ 14 ]. These factors cause the finger vein images to become unstable and have low contrast.…”

Section: Introductionmentioning

confidence: 99%

Intensity Variation Normalization for Finger Vein Recognition Using Guided Filter Based Singe Scale Retinex

Xie

Yoon

et al. 2015

Sensors

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Finger vein recognition has been considered one of the most promising biometrics for personal authentication. However, the capacities and percentages of finger tissues (e.g., bone, muscle, ligament, water, fat, etc.) vary person by person. This usually causes poor quality of finger vein images, therefore degrading the performance of finger vein recognition systems (FVRSs). In this paper, the intrinsic factors of finger tissue causing poor quality of finger vein images are analyzed, and an intensity variation (IV) normalization method using guided filter based single scale retinex (GFSSR) is proposed for finger vein image enhancement. The experimental results on two public datasets demonstrate the effectiveness of the proposed method in enhancing the image quality and finger vein recognition accuracy.

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Feature Component-Based Extreme Learning Machines for Finger Vein Recognition

Cited by 39 publications

References 31 publications

A Systematic Review of Finger Vein Recognition Techniques

A Systematic Review of Finger Vein Recognition Techniques

Imbalanced Learning for Air Pollution by Meta-Cognitive Online Sequential Extreme Learning Machine

Intensity Variation Normalization for Finger Vein Recognition Using Guided Filter Based Singe Scale Retinex

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