Super-resolution for Selfie Biometrics: Introduction and Application to Face and Iris

Alonso‐Fernandez, Fernando; Farrugia, Reuben A.; Fiérrez, Julián; Bigün, Josef

doi:10.1007/978-3-030-26972-2_5

Cited by 14 publications

(8 citation statements)

References 80 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Iris recognition systems have been improved over the last decade achieving better performance [16], more convenient acquisition at a distance [49], and full integration with mobile devices [5]. However, the robustness against attacks is still a challenge for the research community and industrial applications [33].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Introduction to Presentation Attack Detection in Iris Biometrics and Recent Advances

Morales¹,

Fiérrez²,

Galbally³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Iris recognition technology has attracted an increasing interest in the last decades in which we have witnessed a migration from research laboratories to real world applications. The deployment of this technology raises questions about the main vulnerabilities and security threats related to these systems. Among these threats presentation attacks stand out as some of the most relevant and studied. Presentation attacks can be defined as presentation of human characteristics or artifacts directly to the capture device of a biometric system trying to interfere its normal operation. In the case of the iris, these attacks include the use of real irises as well as artifacts with different level of sophistication such as photographs or videos. This chapter introduces iris Presentation Attack Detection (PAD) methods that have been developed to reduce the risk posed by presentation attacks. First, we summarise the most popular types of attacks including the main challenges to address. Secondly, we present a taxonomy of Presentation Attack Detection methods as a brief introduction to this very active research area. Finally, we discuss the integration of these methods into Iris Recognition Systems according to the most important scenarios of practical application.

show abstract

Section: Discussionmentioning

confidence: 99%

“…• The iris is an internal organ of the eye that is externally visible. It can be acquired at a distance and the advances on capture devices allow to easily integrate iris recognition into portable devices [5,49].…”

Section: Introductionmentioning

confidence: 99%

Introduction to Presentation Attack Detection in Iris Biometrics and Recent Advances

Morales¹,

Fiérrez²,

Galbally³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…On the other hand, other classes with low representation (0-2 and 60-99) have better performance, and in some cases, 0-2 even shows the best accuracy. The most represented class (25)(26)(27)(28)(29)(30)(31)(32) does correlate with the best accuracy in some cases, and its performance is among the best in most columns.…”

Section: Summary and Comparison With Previous Workmentioning

confidence: 97%

Facial masks and soft‐biometrics: Leveraging face recognition CNNs for age and gender prediction on mobile ocular images

et al. 2021

Self Cite

View full text Add to dashboard Cite

We address the use of selfie ocular images captured with smartphones to estimate age and gender. Partial face occlusion has become an issue due to the mandatory use of face masks. Also, the use of mobile devices has exploded, with the pandemic further accelerating the migration to digital services. However, state-of-the-art solutions in related tasks such as identity or expression recognition employ large Convolutional Neural Networks, whose use in mobile devices is infeasible due to hardware limitations and size restrictions of downloadable applications. To counteract this, we adapt two existing lightweight CNNs proposed in the context of the ImageNet Challenge, and two additional architectures proposed for mobile face recognition. Since datasets for softbiometrics prediction using selfie images are limited, we counteract over-fitting by using networks pre-trained on ImageNet. Furthermore, some networks are further pretrained for face recognition, for which very large training databases are available. Since both tasks employ similar input data, we hypothesise that such strategy can be beneficial for soft-biometrics estimation. A comprehensive study of the effects of different pretraining over the employed architectures is carried out, showing that, in most cases, a better accuracy is obtained after the networks have been fine-tuned for face recognition. | INTRODUCTIONRecent research has explored the automatic extraction of information such as gender, age, ethnicity, etc. of an individual, known as soft-biometrics [1]. It can be deduced from biometric data like face photos, voice, gait, hand or body images, etc. One of the most natural ways is face analysis [2], but given the use of masks due to the COVID-19 pandemic, the face appears occluded even in cooperative settings, leaving the ocular region as the only visible part. In recent years, the ocular region has gained attention as a stand-alone modality for a variety of tasks, including person recognition [3], softbiometrics estimation [4], or liveness detection [5]. Accordingly, this work is concerned with the challenge of estimating soft-biometrics when only the ocular region is available. Additionally, we are interested in mobile environments [6]. The pandemic has accelerated the migration to the digital domain, converting mobiles in data hubs used for all type of This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

show abstract

“…The proposed inpainting model should utilize the color, texture, and size of eyes and eyebrows to estimate this kind of demographic attributes and inpaint other face elements according to the estimated features. R3) The proposed solution should preserve the identity-related biometric properties present in the eyes regions [38,18] when generating the full face [39]. Noteworthy, this eye region is demonstrated to encode a large part of the identity information present in the face [44] enabling both person recognition and fake face detection [40].…”

Section: Introductionmentioning

confidence: 99%

E2F-GAN: Eyes-to-Face Inpainting via Edge-Aware Coarse-to-Fine GANs

et al. 2022

Self Cite

View full text Add to dashboard Cite

Face inpainting is a challenging task aiming to fill the damaged or masked regions in face images with plausibly synthesized contents. Based on the given information, the reconstructed regions should look realistic and more importantly preserve the demographic and biometric properties of the individual. The aim of this paper is to reconstruct the face based on the periocular region (eyes-to-face). To do this, we proposed a novel GAN-based deep learning model called Eyes-to-Face GAN (E2F-GAN) which includes two main modules: a coarse module and a refinement module. The coarse module along with an edge predictor module attempts to extract all required features from a periocular region and to generate a coarse output which will be refined by a refinement module. Additionally, a dataset of eyes-to-face synthesis has been generated based on the public face dataset called CelebA-HQ for training and testing. Thus, we perform both qualitative and quantitative evaluations on the generated dataset. Experimental results demonstrate that our method outperforms previous learning-based face inpainting methods and generates realistic and semantically plausible images. We also provide the implementation of the proposed approach to support reproducible research via (https://github.com/amiretefaghi/E2F-GAN).

show abstract

Super-resolution for Selfie Biometrics: Introduction and Application to Face and Iris

Cited by 14 publications

References 80 publications

Introduction to Presentation Attack Detection in Iris Biometrics and Recent Advances

Introduction to Presentation Attack Detection in Iris Biometrics and Recent Advances

Facial masks and soft‐biometrics: Leveraging face recognition CNNs for age and gender prediction on mobile ocular images

E2F-GAN: Eyes-to-Face Inpainting via Edge-Aware Coarse-to-Fine GANs

Contact Info

Product

Resources

About