Biometric Identification on Android Smartphones

Darwaish, Shah Faisal; Moradian, Esmiralda; Rahmani, Tirdad; Knauer, Martin

doi:10.1016/j.procs.2014.08.250

Cited by 17 publications

(8 citation statements)

References 9 publications

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“…For example, modalities can be categorized as physiological (ECG, DNA, iris, fingerprint, earlobe), cognitive and behavioral (voice, keystroke, gait, and signature) [115]. The following are the characteristics of all of these biometric modalities [78], [116]: This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.…”

Section: Ecg Signal In Biometricsmentioning

confidence: 99%

A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges

Uwaechia

Ramli

2021

IEEE Access

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Electrocardiogram (ECG) has extremely discriminative characteristics in the biometric field and has recently received significant interest as a promising biometric trait. However, ECG signals are susceptible to several types of noises, such as baseline wander, powerline interference, and high/lowfrequency noises, making it challenging to realize biometric identification systems precisely and robustly. Therefore, ECG signal denoising is a major pre-processing step and plays a crucial role in ECG-based biometric human identification. ECG signal analysis for biometric recognition can combine several steps, such as preprocessing, feature extraction, feature selection, feature transformation, and classification which is a very challenging task. Moreover, the selected success measures and proper structure of the ECG signal database play significant roles in biometric system analysis, considering that publicly available databases are essential by the research community to evaluate the performance of their proposed algorithms. In this survey, we review most of the techniques employed for the ECG as biometrics for human authentication. Firstly, we present an overview and discussion on ECG signal preprocessing, feature extraction, feature selection, and feature transformation for ECG-based biometric systems. Secondly, we present a survey of the available ECG databases to evaluate and compare the acquisition protocol, acquisition hardware, and acquisition resolution (bits) for ECG-based biometric systems. Thirdly, we also present a survey on different techniques, including deep learning methods: deep supervised learning, deep semi-supervised learning, and deep unsupervised learning, for ECG signal classification. Lastly, we present the state-of-art approaches of information fusion in multimodal biometric systems.

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Section: Ecg Signal In Biometricsmentioning

confidence: 99%

A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges

Uwaechia

Ramli

2021

IEEE Access

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“…Biometric user authentication is available on consumer devices, including smartphones, using facial recognition [1]- [3] and fingerprint biometric [4]- [9]. The broad adoption of biometrics on consumer devices was originally discussed in [10] with additional discussion of the impacts in several following articles [11]- [13].…”

Section: Introductionmentioning

confidence: 99%

Deep neural network and data augmentation methodology for off-axis iris segmentation in wearable headsets

2020

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A data augmentation methodology is presented and applied to generate a large dataset of offaxis iris regions and train a low-complexity deep neural network. Although of low complexity the resulting network achieves a high level of accuracy in iris region segmentation for challenging off-axis eye-patches. Interestingly, this network is also shown to achieve high levels of performance for regular, frontal, segmentation of iris regions, comparing favorably with state-of-the-art techniques of significantly higher complexity. Due to its lower complexity this network is well suited for deployment in embedded applications such as augmented and mixed reality headsets. Liu,in [72] proposed two CNN approaches to segment noisy iris images acquired under unconstrained conditions. In the first approach called hierarchical convolutional neural networks (HCNNs), three patches taken from different scales of the same image are used as input. The HCNN consists of three similar blocks, a combination of convolutional and pooling layers that are merged together into a fully connected layer. In the second approach, 31 convolutional layers and 6 pooling layers are used to compose the multi-scale fully convolutional network (MFCNs). Both models are end-to-end, with no requirement for pre-or post-processing of the image. Arsalan [73], introduced a two-stage iris segmentation method. The first stage includes a pre-processing of the image and the use of a modified Hough Transform to identify the region of interest (ROI). In the second stage, a mask of [21 × 21] pixels, based on the ROI defined in the previous stage, is fed to a pre-trained VGG-face model which classifies the pixels as iris or non-iris. In a follow up work which is focused on segmenting low quality iris images, Arsalan in [74], proposed a densely connected fully convolutional network (IrisDenseNet), consisting of two main components: a densely connected encoder and a SegNet decoder. In a similar work, Bazrafkan in [43], presented a network design focused on segmenting iris of inferior quality. Four different end-to-end fully convolutional networks are merged into a single model using a method known as Semi Parallel Deep Neural Networks (SPDNN). In this way, the final model benefits from each of the four distinct network designs. Finally, since the existence of a large labelled dataset is a prerequisite in order to implement a convolutional neural network approach, Jalilian in [75] to overcome this obstacle, introduced a domain adaption method so that a CNN for iris segmentation could be trained with a limited data. ContributionsThe focus of this work is to improve the segmentation of off-axis iris images originating from the unconstrained conditions of a user-facing camera on wearable AR/VR device.The model proposed is an end to end deep neural network which accepts an off-axis eye-region image and generates the corresponding binary segmentation map for the iris region as output. Performance evaluation of the proposed model shows advantages over recent iris segmentation tec...

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“…Owing to the growing popularity of smart devices, security as well as privacy on such devices are of paramount importance since those act as a platform for online commerce besides their involvement in accessing crucial information [1][2][3][4][5]. Over time, smart devices have offered various superior functions that led to the growing interest of users in them but at the same time grew the vulnerability of users to fraud [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…Over time, smart devices have offered various superior functions that led to the growing interest of users in them but at the same time grew the vulnerability of users to fraud [6][7][8]. Accessing such crucial information and transacting securely necessitates safe measures that offer increased user adoptability as well as resistance to impending threats [2]. Ranging from a daily user of a personal computer to corporations, government departments, medical practitioners and businesses, security of file systems together with the protection of system from unprivileged access is a major security concern.…”

Section: Introductionmentioning

confidence: 99%

A Stable and Secure One-Time-Password Generation Mechanism using Fingerprint Features

Lone¹,

Mir²

2019

IJITEE

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The growth of online application used for financial transactions and transferring personal information is increasingly common on internet and in mobile communication. These applications require authenticating legitimate users by assigning digital identities. Static passwords are perhaps most common type of credentials used today to authenticate the users. To avoid tedious task of remembering passwords, users often behave less securely by using low entropy and weak passwords, thus presenting security threats to online services. Various solutions have been provided to eliminate the need of users to create and manage passwords’ typical solution is based on generating one time password (OTP) for a single session or transaction. Unfortunately in most of the general mechanisms used for generating one time password (OTP) randomness of OTP system breaks after certain period of time and hence passwords become predictable. To solve this problem, in this paper a novel OTP generation method has been proposed, which generates OTP from fingerprint features of the user. The OTP produced from the system is secure as it uses fingerprint features in the seed and RIPEMD160 hash function in OTP generation procedure.

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Biometric Identification on Android Smartphones

Cited by 17 publications

References 9 publications

A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges

A Comprehensive Survey on ECG Signals as New Biometric Modality for Human Authentication: Recent Advances and Future Challenges

Deep neural network and data augmentation methodology for off-axis iris segmentation in wearable headsets

A Stable and Secure One-Time-Password Generation Mechanism using Fingerprint Features

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