A survey of homeland security biometrics and forensics research

Nagaraju, Vidhyashree; Fiondella, Lance

doi:10.1109/ths.2016.7568932

Cited by 3 publications

(2 citation statements)

References 75 publications

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“…Hence, it is crucial that a biometric authentication system be implemented to provide the shortest process time, with minimal user involvement. Delays can be shortened and the economic burden lightened if swift and secure systems were available [17].…”

Section: Introductionmentioning

confidence: 99%

MS-Net: Multi-Segmentation Network for the Iris Region Using Deep Learning in an Unconstrained Environment

et al. 2023

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Iris segmentation is a significant phase in the iris recognition process because segmentation errors cascade into all subsequent phases. Therefore, it is important that errors in iris segmentation are minimised. The U-Net architecture that uses a deep learning approach was previously adopted for this task, but its performance was affected by the deformation of iris images caused by various noise factors in unconstrained (non-ideal) environments. Scratches, blurriness, dirt, specular reflections and other noise factors are some of the challenges faced in unconstrained environments when eyeglasses are present in the original images. Additionally, the performance of the iris segmentation was degraded due to problems of exploding gradient or vanishing gradient and the loss of information. This paper proposes a multisegmentation network called MS-Net, based on a deep learning approach, that aims to capture high-level semantic features while maintaining spatial information to improve the accuracy of iris segmentation. MS-Net consists of three principal segments: a feature encoder network, a multi-scale context feature extractor network (MSCFE-Net) and a feature decoder network. MSCFE-Net a multi-scale context feature extractor network is constructed from a dilated residual multi-convolutional network module and a pyramid pooling residual model based on an attention convolutional module. In addition, the proposed MS-Net contains dense connections within the feature decoder network to decrease training difficulty, by using only a few training samples. The accuracy of MS-Net was evaluated on the CASIA-Iris.V4-1000 and UBIRIS.V2 databases. The performance of our proposed MS-Net method on the CASIA-Iris.V4-1000 and UBIRIS.V2 databases achieved an overall accuracy of 97.11% and 96.128%, respectively. Experiment results show that MS-Net is able to achieve better results compared to earlier methods used for the same purpose.

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Section: Introductionmentioning

confidence: 99%

MS-Net: Multi-Segmentation Network for the Iris Region Using Deep Learning in an Unconstrained Environment

et al. 2023

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“…fingerprint and face [2]. Starting from the 1990s, an increasing interest from both industry and academia has ignited research to design and exploit biometric recognition systems as an alternative to authentication methods relying on the use of passwords or tokens [3].…”

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confidence: 99%

Biometric Recognition Based on Hand Electromagnetic Scattering at Microwaves

Maiorana

Ramaccia

Stefanini

et al. 2023

IEEE Trans. Microwave Theory Techn.

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In recent years, the need for secure, reliable, and easy-to-use automatic recognition approaches has favored the diffusion of biometric systems. Such methods exploit individuals' physical or behavioral characteristics to differentiate between legitimate users and potential impostors. In this article, we evaluate the feasibility of recognizing a subject by exploiting the electromagnetic (EM) scattering from the user's hand or wrist when it interacts with an EM field at microwave (MW) frequencies. In detail, the proposed recognition framework requires a subject to place a hand between two antennas emitting at frequencies in the X-band, i.e., between 8.2 and 12.4 GHz. The measured scattering parameters are employed as discriminative characteristics to perform user recognition. To improve the achievable recognition performance, an EM model of the involved body parts has been studied and used to design a metasurface (MTS) wristband that could emphasize subject-specific characteristics. The effectiveness of the proposed approach has been tested by collecting multiple measurements from 43 subjects during two distinct acquisition sessions. The obtained results testify that the proposed approach can be used either to implement a robust stand-alone biometric system or exploited together with other well-established hand characteristics such as fingerprint, palm print, or hand vein to improve the achievable recognition performance.

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