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AI Case Study

The EU to pilot automated border-control system to enhance security at borders

In an attempt to uphold strict security protocols at borders, the EU will enhance border controls by introducing iBorderCtrl in Hungary, Greece, Latvia for a pilot of nine months until the end of August 2019. The system comprises of two phases, where travellers from outside the EU are required to firstly complete a registration process that includes providing documents and information and then, once at the borders, to engage in video-audio conversion with an 'avatar' that carries out the check. The system also validates the biometric identity of the traveller through fingerprints, palm vein images and face matching. Finally, the Automatic Deception Detection System (ADDS) that consists of an avatar asking a series of questions is aimed at quantifying the probability of deceit in interview by analysing non-verbal micro expressions.


Public And Social Sector


Project Overview

"iBorderCtrl complete two-stage solution is currently under lab testing and all individual components and the system as a whole is being tested. In the upcoming period, the actual commencement of piloting deployment will start with approximately nine months duration (end in August 2019) in Hungarian, Greek and Latvian land borders (multiple iterations of testing and validation will be performed). Specific border crossing points have been selected for this purpose where the integrated iBorderCtrl prototype and the overall platform will be tested on real time. A large full scale evaluation will take place where apart from the technical performance the usability along with the wider user experience features will be examined.

The two phase procedure:

At pre-registration step, travellers self-report at the comfort of their own home through an on-line system that collects all relevant data helping them ensure they are fulfilling their obligations and allowing for all automated checks to take place in advance, allowing for much more computationally expensive methods to be deployed. It also provides an assessment, for the case e.g. where some aspects of human error in document preparation and collection can be detected by the traveller, to inform and provide with the opportunity to correct it prior to their crossing. Information shared with the traveller is limited to that, where a potentially criminal activity being planned would not benefit from the pre-crossing checks. While identified potentially criminal crossings are flagged to the border guards allowing them to target those individuals for further targeted evaluation. This phase enables the automation of deception detection, document authentication, external database correlation, face matching and advanced risk modelling methodologies to be deployed before the traveller even gets to the border and therefore without increasing the time the border guard spends per traveller. Deception detection is a novel approach for border control deployment applications, which if considered as self-deployed through the pre-registration phase approach, it may be able to evaluate travellers, who cannot be evaluated deterministically by other methods, effectively. Thus it may prove to be a key enabler in identifying subjects that border guards should pay special attention to during the actual crossing, and an indicator of those aspects of their travel that are suspicious.

At the border crossing stage iBorderCtrl provides key technology to the border guards both integrated to existing static installations, as well as a portable hardware platform that empowers -through technology- the border guard. At this stage all information of the traveller gathered during the pre-registration phase is now available to the Border Guard with iBorderCtrl bringing all analytic results from each technology together to identify risks to the agent that support him in both an overall evaluation of the traveller, as well as highlighting specific potential issues the agent should focus on. All documents and required information needed to cross the border are re-evaluated at the border crossing in their original hard form, however as the check has already been performed the checks are for the most part limited to validating that indeed the originals contain the same information as what was collected at pre-registration. Moreover, the biometric checks (fingerprints, palm vein face matching) take place for identity verification of the traveller. Furthermore, in case that the traveller wishes to cross the border with a vehicle, an additional check is performed to detect hidden humans inside the vehicle." (

Reported Results

"The automated lie-detection system was modeled after another system created by some individuals from iBorderCtrl’s team, but it was only tested on 30 people. In this test, half of the people told the truth while the other half lied to the virtual agent. It had about a 76 percent accuracy rate, and that doesn’t take into consideration the variances in being told to lie versus earnestly lying.

Keeley Crockett at Manchester Metropolitan University, UK, and a member of the iBorderCtrl team, said that they are “quite confident” they can bring the accuracy rate up to 85 percent. But more than 700 million people travel through the EU every year, according to the European Commission, so that percentage would still lead to a troubling number of misidentified “liars” if the system were rolled out EU-wide." (Gizmodo)

The actual commencement of piloting deployment will start with approximately nine months duration, in December 2018, thus results are not yet available.


"The Automatic Deception Detection System (ADDS)* performs, controls and assesses the pre-registration interview by sequencing a series of questions posed to travellers by an Avatar. ADDS quantifies the probability of deceit in interviews by analysing interviewees non-verbal micro expressions. This, coupled with an avatar, moves this novel approach to deception detection to the pre-registration phase resulting in its deployment without an impact to the time spend at the border crossing by the traveller.

The work started with interviewing psychologists and reviewing the psychology literature to find a pool of candidate features to which machine learning could be applied to answer the research questions. Thus, there is not an explicit model. There is however, an overall conceptual model showing there are drivers of non-verbal behaviour that create inconsistencies with truthful NVB (detectable through machine learning) when an interviewee is deceptive. These include (but are not limited to) Arousal (including “stress” and “duping delight”), cognitive load and behaviour control."





"More than 700 million people enter the EU every year – a number that is rapidly rising. The huge volume of travellers and vehicles is piling pressure on external borders, making it increasingly difficult for border staff to uphold strict security protocols – checking the travel documents and biometrics of every passenger – whilst keeping disruption to a minimum." (



Data from documents submitted by individuals during registration and biometric data such as fingerprints, palm vein face matching.

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