Feature level fusion using palmprint and finger geometry based on Canonical Correlation Analysis

Yu, Pengfei; Xu, Dan; Zhou, Hao

doi:10.1109/icacte.2010.5579795

Cited by 14 publications

(6 citation statements)

References 9 publications

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“…Hand images contain rich features, which could be utilized in terms of biometric identification, verification or classification. Examples of these are hand geometry [11] and finger geometry [12]. Despite the geometrical features being easy to acquire, they still result in low recognition performance and they are usually fused with other biometrics to enhance performance.…”

Section: Introductionmentioning

confidence: 99%

Finger texture biometric verification exploiting Multi-scale Sobel Angles Local Binary Pattern features and score-based fusion

Al-Nima

Abdullah

Al-Kaltakchi

et al. 2017

Digital Signal Processing

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Section: Introductionmentioning

confidence: 99%

Finger texture biometric verification exploiting Multi-scale Sobel Angles Local Binary Pattern features and score-based fusion

Al-Nima

Abdullah

Al-Kaltakchi

et al. 2017

Digital Signal Processing

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“…Threshold is determined by using the Euclidean, Absolute, Hamming or Mahalanobis distance metrics. The AND/OR rule is a famous one, 7 and the majority of the voting algorithm has been implemented by Li et al 28 It is clear from Table 1 that score level and decision level have been explored reasonably, and feature level is given more attention. The rank level is not exploited yet using hand geometry.…”

Section: Levels Of Fusionmentioning

confidence: 99%

“…Other than considering all¯ngers or the entire hand, Zhou et al has experimented particular nger geometry. 28 For higher accuracy, multimodal systems are developed using fusion at various levels. In multimodal hand geometry, most of the techniques are integrated with other hand-based traits (e.g.¯ngerprint, palmprint, vein pattern, etc.).…”

Section: Introductionmentioning

confidence: 99%

“…10 Decision level fusion using majority voting, AND, OR rule with 86 users (10 images per user) has been implemented. 28 Hand shape-based approach using Independent Component Analysis (ICA1 and ICA2) has been presented with 458 right hand subjects. 27 In this work, hand mode has been used with low cost scanner to minimize computational complexities in terms of feature de¯nition and classi¯cation algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Fusion-Based Hand Geometry Recognition Using Dempster–Shafer Theory

Bera

Bhattacharjee

Nasipuri

2015

Int. J. Patt. Recogn. Artif. Intell.

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This paper presents a new technique for user identi¯cation and recognition based on the fusion of hand geometric features of both hands without any pose restrictions. All the features are extracted from normalized left and right hand images. Fusion is applied at feature and also at decision level. Two probability-based algorithms are proposed for classi¯cation. The¯rst algorithm computes the maximum probability for nearest three neighbors. The second algorithm determines the maximum probability of the number of matched features with respect to a thresholding on distances. Based on these two highest probabilities initial decisions are made. The¯nal decision is considered according to the highest probability as calculated by the Dempster-Shafer theory of evidence. Depending on the various combinations of the initial decisions, three schemes are experimented with 201 subjects for identi¯cation and veri¯cation. The correct identi¯cation rate is found to be 99.5%, and the false acceptance rate (FAR) of 0.625% has been found during veri¯cation. Int. J. Patt. Recogn. Artif. Intell. 2015.29. Downloaded from www.worldscientific.com by UNIVERSITY OF OTAGO on 07/12/15. For personal use only.knowledge-based (password) or token-based (PIN) user veri¯cation systems in various required levels (low to high level) of security intelligence. 9 It is applied as one of the best reliable and legitimate human authentication systems in a constrained environment. The primary objective is to discriminate the identity of a person based on various unique biometric properties recognized by an automated system. These systems are developed by the physical (e.g. face,¯ngerprint, hand geometry, hand vein, etc.) and behavioral (e.g. gait, signature, voice, etc.) distinctiveness of an individual. 10 Di®erent human organs are employed individually (unimodal) or combined (multimodal) for this purpose. A standalone recognition decision by a unimodal system is not always reliable and robust enough to verify whether a person is genuine or imposter. So to enhance the performance, fusion 17 is very useful to authenticate an individual with multibiometrics. It basically combines several decisions taken by various expert systems. Multibiometric systems provide certain bene¯ts over the limitations of unimodal systems such as noise-e®ect, intra-class variations, non-universality, spoof-attack and°exibility. 9 Di®erent multibiometric systems are accessible and are classi¯ed according to their basic properties (e.g. multi-sensor) and level(s) of implementation (e.g. decision level). An enormous number of biometric systems with di®erent characteristics and functionalities are running successfully worldwide for several decades in the government (National ID cards), forensics (criminal investigations) and commercial applications (smart cards). 19

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“…They have been widely discovered over the recent years by exploiting different biometric characteristics such as the earprint [1], palmprint [2], face [3,4], sclera [5], iris [6,7], speaker [8][9][10][11], backhand patterns [12] and FT [13,14]. Other studies concentrated on more than one characteristic such as [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Finger Texture Verification Systems Based on multiple spectrum Lighting Sensors with Four Fusion Levels

Al-Kaltakchi

Omar

Abdullah

et al. 2019

Iraqi Journal of ICT

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Finger Texture (FT) is one of the most recent attractive biometric characteristic. Itrefers to a finger skin area which is restricted between the fingerprint and the palm print (just after including the lower knuckle). Different specifications for the FT can be obtained by employing multiple images spectrum of lights. This inspired the insight of applying a combination between the FT features that acquired by utilizing two various spectrum lightings in order to attain high personal recognitions. Four types of fusion will be listed and explained here: Sensor Level Fusion (SLF), Feature Level Fusion (FLF), Score Level Fusion (ScLF) and Decision Level Fusion (DLF). Each fusion method is employed and examined for an FT verification system. From the database of Multiple Spectrum CASIA (MSCASIA), FT images have been collected. Two types of spectrum lights have been exploited (the wavelength of 460 nm, which represents a Blue (BLU) light, and the White (WHT) light). Supporting comparisons were performed, including the state-of-the-art. Best recognition performance were recorded for the FLF based concatenation rule by improving the Equal Error Rate (EER) percentages from 5% for the BLU and 7% for the WHT to 2%.

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Feature level fusion using palmprint and finger geometry based on Canonical Correlation Analysis

Cited by 14 publications

References 9 publications

Finger texture biometric verification exploiting Multi-scale Sobel Angles Local Binary Pattern features and score-based fusion

Finger texture biometric verification exploiting Multi-scale Sobel Angles Local Binary Pattern features and score-based fusion

Fusion-Based Hand Geometry Recognition Using Dempster–Shafer Theory

Finger Texture Verification Systems Based on multiple spectrum Lighting Sensors with Four Fusion Levels

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