Automated border control e-gates and facial recognition systems

Río, José Sánchez del; Moctezuma, Daniela; Conde, Cristina; Diego, Isaac Martín de; Cabello, Enrique

doi:10.1016/j.cose.2016.07.001

Cited by 80 publications

(31 citation statements)

References 16 publications

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“…Besides that, some of the automated gate system in the market does not provide any monitoring tool or interface to monitor the gate system [11] [12]. Some of them are also unable to open the gate manually if there are any problems to www.ijacsa.thesai.org the system.…”

Section: Related Workmentioning

confidence: 99%

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Implementation of Intelligent Automated Gate System with QR Code

Hamid¹,

Gee²,

Bahaman³

et al. 2018

ijacsa

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This paper is about QR code-based automated gate system. The aim of the research is to develop and implement a type of medium-level security gate system especially for small companies that cannot afford to install high-tech auto gate system. IAGS is a system that uses valid staffs' QR code pass card to activate the gate without triggering the alarm. It is developed to connect to the internet and provide a real-time email notification if any unauthorized activities detected. Besides that, it is also designed to record all the incoming and outgoing activities for all staff. All QR code pass cards that are generated to staff will be encrypted to provide integrity to the data. The system is based on items such as PIR motion sensor, servo motor, Arduino microcontroller, Piezo buzzer, and camera. The software is implemented using VB.NET and the QR recognition level is about 99% accurate.

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Section: Related Workmentioning

confidence: 99%

“…Most of the existing gate systems also do not support online connection [11]. The gate system only trigger the alarm without sending out any notification/email to the relevant department.…”

Section: Related Workmentioning

confidence: 99%

Implementation of Intelligent Automated Gate System with QR Code

Hamid¹,

Gee²,

Bahaman³

et al. 2018

ijacsa

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show abstract

“…A current application of face verification systems in real environments with considerable security constraints is ABC systems [ 1 , 2 ]. These are used to verify passenger identities automatically through biometrics [ 3 , 4 ]. Due to this technology, border guards are helped by ABC systems to carry out routine tasks such as passport control and face verification at the international border-crossing points (BCP) of several countries in shorter times and with standard and homogeneous results.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network Approach for Multispectral Facial Presentation Attack Detection in Automated Border Control Systems

Sánchez‐Sánchez

Conde

Gómez-Ayllón

et al. 2020

Entropy

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Automated border control systems are the first critical infrastructure point when crossing a border country. Crossing border lines for unauthorized passengers is a high security risk to any country. This paper presents a multispectral analysis of presentation attack detection for facial biometrics using the learned features from a convolutional neural network. Three sensors are considered to design and develop a new database that is composed of visible (VIS), near-infrared (NIR), and thermal images. Most studies are based on laboratory or ideal conditions-controlled environments. However, in a real scenario, a subject’s situation is completely modified due to diverse physiological conditions, such as stress, temperature changes, sweating, and increased blood pressure. For this reason, the added value of this study is that this database was acquired in situ. The attacks considered were printed, masked, and displayed images. In addition, five classifiers were used to detect the presentation attack. Note that thermal sensors provide better performance than other solutions. The results present better outputs when all sensors are used together, regardless of whether classifier or feature-level fusion is considered. Finally, classifiers such as KNN or SVM show high performance and low computational level.

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“…Automatic face recognition is an important field and has a tremendous impact on many domains of our life. For example, private images can be sorted by persons that appear on them (e.g., Apple Photos or Google Photos), or airports perform automatic face recognition as passport control [29]. As the latter has severe security implications, most face recognition challenges such as the Face Recognition Vendor Tests 1 evaluate algorithms that perform verification, i.e., where a pair of model and probe images is tested whether they contain the same identity.…”

Section: Introductionmentioning

confidence: 99%

Unconstrained Face Detection and Open-Set Face Recognition Challenge

Günther

Herrmann

et al. 2017

2017 IEEE International Joint Conference on Biometrics (IJCB)

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Face detection and recognition benchmarks have shifted toward more difficult environments. The challenge presented in this paper addresses the next step in the direction of automatic detection and identification of people from outdoor surveillance cameras. While face detection has shown remarkable success in images collected from the web, surveillance cameras include more diverse occlusions, poses, weather conditions and image blur. Although face verification or closed-set face identification have surpassed human capabilities on some datasets, open-set identification is much more complex as it needs to reject both unknown identities and false accepts from the face detector. We show that unconstrained face detection can approach high detection rates albeit with moderate false accept rates.By contrast, open-set face recognition is currently weak and requires much more attention.

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Automated border control e-gates and facial recognition systems

Cited by 80 publications

References 16 publications

Implementation of Intelligent Automated Gate System with QR Code

Implementation of Intelligent Automated Gate System with QR Code

Convolutional Neural Network Approach for Multispectral Facial Presentation Attack Detection in Automated Border Control Systems

Unconstrained Face Detection and Open-Set Face Recognition Challenge

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