Selfie Periocular Verification Using an Efficient Super-Resolution Approach

Tapia, Juan; Gomez‐Barrero, Marta; Lara, Rodrigo; Valenzuela, Andrés; Busch, Christoph

doi:10.1109/access.2022.3184301

Cited by 11 publications

(5 citation statements)

References 31 publications

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“…The widespread use of smartphones has prompted the development of novel remote authentication systems embedded in applications that require the input of biometric data such as fingerprints, faces, iris [21], and selfies [22]. Additionally, many services require digital photographs of ID cards, which are often captured with smartphones, as part of their digital onboarding process [23,24].…”

Section: B Fake Id Detectionmentioning

confidence: 99%

Open-Set: ID Card Presentation Attack Detection Using Neural Style Transfer

Markham,

López,

Nieto-Hidalgo

et al. 2024

IEEE Access

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The accurate detection of ID card Presentation Attacks (PA) is becoming increasingly important due to the rising number of online/remote services that require the presentation of digital photographs of ID cards for digital onboarding or authentication. Furthermore, cybercriminals are continuously searching for innovative ways to fool authentication systems to gain unauthorized access to these services. Although advances in neural network design and training have pushed image classification to the state of the art, one of the main challenges faced by the development of fraud detection systems is the curation of representative datasets for training and evaluation. The handcrafted creation of representative presentation attack samples often requires expertise and is very time-consuming, thus an automatic process of obtaining high-quality data is highly desirable. This work explores ID card Presentation Attack Instruments (PAI) in order to improve the generation of samples with four Generative Adversarial Networks (GANs) based image translation models and analyses the effectiveness of the generated data for training fraud detection systems. Using open-source data, we show that synthetic attack presentations are an adequate complement for additional real attack presentations, where we obtain an EER performance increase of 0.63 % points for print attacks and a loss of 0.29 % for screen capture attacks.

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Section: B Fake Id Detectionmentioning

confidence: 99%

Open-Set: ID Card Presentation Attack Detection Using Neural Style Transfer

Markham,

López,

Nieto-Hidalgo

et al. 2024

IEEE Access

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“…In the other hand, there have also been some implementations on smartphones. Raja et al [25], and Tapia et al [26] utilised an iPhone 5S and a Nokia Lumia 1020 as the capture device using Visible spectrum (VS) imaging and superresolution. The smartphone also produced segmentation inference and recognition.…”

Section: Related Work a Iris Recognition Hardwarementioning

confidence: 99%

Toward an Efficient Iris Recognition System on Embedded Devices

Benalcazar,

Tapia,

Vasquez

et al. 2023

IEEE Access

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Iris Recognition (IR) is one of the market's most reliable and accurate biometric systems. Today, it is challenging to build NIR-capturing devices under the premise of hardware price reduction. Commercial NIR sensors are protected from modification. The process of building a new device is not trivial because it is required to start from scratch with the process of capturing images with quality, calibrating operational distances, and building lightweight software such as eyes/iris detectors and segmentation subsystems. In light of such challenges, this work aims to develop and implement iris recognition software in an embedding system and calibrate NIR in a contactless binocular setup. We evaluate and contrast speed versus performance obtained with two embedded computers and infrared cameras. Further, a lightweight segmenter sub-system called "Unet_xxs" is proposed, which can be used for iris semantic segmentation under restricted memory resources. The evaluations reveal that Unet_xxs reduces the number of parameters by 77% and duplicates the speed of state-of-the-art segmentation models with an EER drop smaller than 1% in Iris Recognition with 8.06 fps and IOU of 0.8382. INDEX TERMSEmbedding systems, Iris sensor, NIR Camera, Hardware.

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“…J. E. Tapia et al, [1] present an Efficient Single Image Super-Resolution calculation, which considers a compromise between the productivity and the size of its channels. Keeping that in mind, the strategy executes a misfortune capability in view of the Sharpness metric used to assess iris pictures quality.…”

Section: Literature Surveymentioning

confidence: 99%

An Extensive Comparative Analysis on Various Efficient Techniques for Image Super-Resolution

Bhujade¹,

Asthana²

2022

IJETAE

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The image up scaling and super resolution gain a attention now a days. Create a high-resolution image with help of a low-resolution image uses in many of the application. The optimization based super-resolution methods is principally driven by the choice of the objective function. Recently artificial intelligence based machine and deep learning methods are also using for the image processing application. It makes easy and efficient way of image super resolution. Recent work has generally centered around limiting the mean squared reproduction blunder. The subsequent evaluations have high pinnacle signal-tocommotion proportions, however they are much of the time lacking high-recurrence subtleties and are perceptually subpar as in they neglect to match the constancy expected at the higher resolution. This paper presents the comparative analysis of the efficient techniques for image super-resolution. Keywords—Image, Super, Resolution, AI, Machine learning, Deep Learning, Perfromance.

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Selfie Periocular Verification Using an Efficient Super-Resolution Approach

Cited by 11 publications

References 31 publications

Open-Set: ID Card Presentation Attack Detection Using Neural Style Transfer

Open-Set: ID Card Presentation Attack Detection Using Neural Style Transfer

Toward an Efficient Iris Recognition System on Embedded Devices

An Extensive Comparative Analysis on Various Efficient Techniques for Image Super-Resolution

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