[Invited Paper] Recent Advances in Biometric Recognition

Ito, Koichi; Aoki, Takafumi

doi:10.3169/mta.6.64

Cited by 13 publications

(8 citation statements)

References 59 publications

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“…Ito et al [17] commented on how researchers seek "new biometric traits to enhance the accuracy and convenience of biometric recognition" suggesting that modalities differ in their performance. Each modality is unique and with that brings an assortment of 'influencing factors' to contend with, although some of these factors are common between specific traits.…”

Section: Factor #I: Modalitiesmentioning

confidence: 99%

“…While exploring recent advancements in biometric recognition, Ito et al [17] states, "biometric techniques [that are] to be used in the practical system depend heavily on application requirements".…”

Section: Factor #V: System Constraintsmentioning

confidence: 99%

“…Algorithms are being designed to produce higher accuracy results and overcome certain environmental factors. Face image processing is a significant research topic and covers many fields including computer vision, pattern recognition, image processing and biometrics as surveyed by Ito et al [17] in which they state, "a variety of face image processing methods has been proposed since the performance of face image processing is significantly influenced by environmental changes such as head pose, expression and illumination changes".…”

Section: Factor #Vii: Algorithmsmentioning

confidence: 99%

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Exploring Mobile Biometric Performance Through Identification of Core Factors and Relationships

Boakes

Guest

Deravi

et al. 2019

IEEE Trans. Biom. Behav. Identity Sci.

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Section: Factor #I: Modalitiesmentioning

confidence: 99%

Section: Factor #V: System Constraintsmentioning

confidence: 99%

Section: Factor #Vii: Algorithmsmentioning

confidence: 99%

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Exploring Mobile Biometric Performance Through Identification of Core Factors and Relationships

Boakes

Guest

Deravi

et al. 2019

IEEE Trans. Biom. Behav. Identity Sci.

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“…There are several biometric characteristics (e.g., DNA, face, and palmprint) that can be exploited in authentication systems [1]. Each one has its own pros and cons, and therefore there is no single optimal characteristic that satisfies all application requirements [2]. Consequently, based on the constraints and conditions of the operational mode, one or more biometric characteristics can be applied.…”

Section: Introductionmentioning

confidence: 99%

Deep Region of Interest and Feature Extraction Models for Palmprint Verification Using Convolutional Neural Networks Transfer Learning

Izadpanahkakhk

Razavi

Taghipour-Gorjikolaie

et al. 2018

Applied Sciences

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Palmprint verification is one of the most significant and popular approaches for personal authentication due to its high accuracy and efficiency. Using deep region of interest (ROI) and feature extraction models for palmprint verification, a novel approach is proposed where convolutional neural networks (CNNs) along with transfer learning are exploited. The extracted palmprint ROIs are fed to the final verification system, which is composed of two modules. These modules are (i) a pre-trained CNN architecture as a feature extractor and (ii) a machine learning classifier. In order to evaluate our proposed model, we computed the intersection over union (IoU) metric for ROI extraction along with accuracy, receiver operating characteristic (ROC) curves, and equal error rate (EER) for the verification task.The experiments demonstrated that the ROI extraction module could significantly find the appropriate palmprint ROIs, and the verification results were crucially precise. This was verified by different databases and classification methods employed in our proposed model. In comparison with other existing approaches, our model was competitive with the state-of-the-art approaches that rely on the representation of hand-crafted descriptors. We achieved a IoU score of 93% and EER of 0.0125 using a support vector machine (SVM) classifier for the contact-based Hong Kong Polytechnic University Palmprint (HKPU) database. It is notable that all codes are open-source and can be accessed online.

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“…have been commercially available and used in access control, ATM, etc. K. Ito et al [1] identified several special features as biometric traits such as: Universality, Uniqueness, Permanence and Measurability.…”

Section: Introductionmentioning

confidence: 99%

Texture Based Vein Biometrics for Human Identification: A Comparative Study

Bashar¹,

Murshed

2018

2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC)

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Hand vein biometric is an important modality for human authentication and liveness detection in many applications. Reliable feature extraction is vital to any biometric system. Over the past years, two major categories of vein features, namely vein structures and vein image textures, were proposed for hand dorsal vein based biometric identification. Of them, texture features seem important as it can combine skin microtextures along with vein properties. In this study, we have performed a comparative study to identify potential texture features and feature-classifier combination that produce efficient vein biometric systems. Seven texture features (HOG, GABOR, GLCM, SSF, DWT, WPT, and LBP) and three multiclass classifiers (LDA, ESVM, and KNN) were explored towards the supervised identification of human from vein images. An experiment with 400 infrared (IR) hand images from 40 adults indicates the superior performance of the histogram of oriented gradients (HOG) and simple local statistical feature (SSF) with LDA and ESVM classifiers in terms of average accuracy (> 90%), average Fscore (> 58%) and average specificity (>93%). The decision-level fusion of the LDA and ESVM classifier with single texture features showed improved performances (by 2.2 to 13.2% of average Fscore) over individual classifier for human identification with IR hand vein images.

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[Invited Paper] Recent Advances in Biometric Recognition

Cited by 13 publications

References 59 publications

Exploring Mobile Biometric Performance Through Identification of Core Factors and Relationships

Exploring Mobile Biometric Performance Through Identification of Core Factors and Relationships

Deep Region of Interest and Feature Extraction Models for Palmprint Verification Using Convolutional Neural Networks Transfer Learning

Texture Based Vein Biometrics for Human Identification: A Comparative Study

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