Facial attributes for active authentication on mobile devices

Samangouei, Pouya; Patel, Vishal M.; Chellappa, Rama

doi:10.1016/j.imavis.2016.05.004

Cited by 58 publications

(37 citation statements)

References 15 publications

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“…Therefore, how to keep attributes from the same identity consistent, while taking full advantage of information for capturing features with multiple views are important questions for the future. Second, biometric verification [32,30,24,94,114] is a developing application for digital mobile devices to resist various attacks in the real world. Compared with full-face based biometric verification [24,30], facial attributes contain more detailed characteristics and can better facilitate active authentication.…”

Section: Model-based Methodsmentioning

confidence: 99%

“…Considering that facial attributes contain more detailed characteristics than the full face, we believe that facial attributes would bring new opportunities for biometric identification in real-world applications. Samangouei et al [94] have attempted the active authentication of mobile devices by facial attributes. A set of binary attribute classifiers are trained to estimate whether attributes are present in images of the current user in a mobile device.…”

Section: Applicationsmentioning

confidence: 99%

“…However, Samangouei et al [94] extract traditional features, such as the LBP feature, which are not taskspecific for attribute estimation and less discriminative than deep features. To some extent, these traditional features and SVM classifiers balances the verification accuracy and mobile performance, whereas other methods with satisfactory performance might have tremendous computation or memory costs.…”

Section: Applicationsmentioning

confidence: 99%

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A Survey of Deep Facial Attribute Analysis

et al. 2020

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Facial attribute analysis has received considerable attention when deep learning techniques made remarkable breakthroughs in this field over the past few years. Deep learning based facial attribute analysis consists of two basic sub-issues: facial attribute estimation (FAE), which recognizes whether facial attributes are present in given images, and facial attribute manipulation (FAM), which synthesizes or removes desired facial attributes. In this paper, we provide a comprehensive survey of deep facial attribute analysis from the perspectives of both estimation and manipulation.First, we summarize a general pipeline that deep facial attribute analysis follows, which comprises two stages: data preprocessing and model construction. Additionally, we introduce the underlying theories of this twostage pipeline for both FAE and FAM. Second, the datasets and performance metrics commonly used in facial attribute analysis are presented. Third, we create a taxonomy of state-of-the-art methods and review deep FAE and FAM algorithms in detail. Furthermore, several additional facial attribute related issues are introduced, as well as relevant real-world applications. Finally, we discuss possible challenges and promising future research directions.

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Section: Model-based Methodsmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

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A Survey of Deep Facial Attribute Analysis

et al. 2020

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“…To provide the active authentication on mobile devices, Samangouei et al [88] introduced the concept of facial attributes.…”

Section: B Channel-based Authentication Schemesmentioning

confidence: 99%

Authentication schemes for smart mobile devices: threat models, countermeasures, and open research issues

et al. 2019

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This paper presents a comprehensive investigation of authentication schemes for smart mobile devices. We start by providing an overview of existing survey articles published in the recent years that deal with security for mobile devices. Then, we give a classification of threat models in smart mobile devices in five categories, including, identity-based attacks, eavesdropping-based attacks, combined eavesdropping and identity-based attacks, manipulation-based attacks, and service-based attacks. This is followed by a description of multiple existing threat models. We also provide a classification of countermeasures into four types of categories, including, cryptographic functions, personal identification, classification algorithms, and channel characteristics. Therefore, according to the countermeasure characteristic used and the authentication model, we categorize the authentication schemes for smart mobile devices in four categories, namely, 1) biometric-based authentication schemes, 2) channelbased authentication schemes, 3) factors-based authentication schemes, and 4) ID-based authentication schemes. In addition, we provide a taxonomy and comparison of authentication schemes for smart mobile devices in form of tables. Finally, we identify open challenges and future research directions.To conduct the literature review, we followed the same process used in our previous work [14].Specifically, the identification of literature for analysis in this paper was based on a keyword search, namely, "authentication scheme", "authentication protocol", "authentication system", and "authentication framework". By searching these keywords in academic databases such as SCO-PUS, Web of Science, IEEE Xplore Digital Library, and ACM Digital Library, an initial set of relevant sources were located. Firstly, only proposed authentication schemes for smart mobile devices were collected. Secondly, each collected source was evaluated against the following criteria: 1) reputation, 2) relevance, 3) originality, 4) date of publication (between 2007 and 2018), and 5) most influential papers in the field. The final pool of papers consists of the most important papers in the field of mobile devices that focus on the authentication as their objective.Our search started on 01/11/2017 and continued until the submission date of this paper. The main contributions of this paper are:• We discuss the existing surveys on security for smart mobile devices.• We classify the threat models, which are considered by the authentication schemes in smart mobile devices, into five main categories, namely, identity-based attacks, eavesdropping-based attacks, combined eavesdropping and identity-based attacks, manipulationbased attacks, and service-based attacks.• We review existing research on countermeasures and security analysis techniques in smart mobile devices.• We provide a taxonomy and a side-by-side comparison, in a tabular form, of the stateof-the-art on the recent advancements towards secure and authentication schemes in smart mobile devices with respec...

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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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Facial attributes for active authentication on mobile devices

Cited by 58 publications

References 15 publications

A Survey of Deep Facial Attribute Analysis

A Survey of Deep Facial Attribute Analysis

Authentication schemes for smart mobile devices: threat models, countermeasures, and open research issues

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

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