Biometric‐enabled watchlists technology

Abstract

For Entry-Exit technologies, such as US VISIT and Smart Borders (e-borders), a watchlist normally contains highquality biometric traits and is checked only against visitors. The situation can change drastically if low-quality images are added into the watchlist. Motivated by this fact, we introduce a systematic approach to assessing the risk of travellers using a biometric-enabled watchlist where some latency of the biometric traits is allowed. The main results presented herein include: (1) a taxonomical view of the watchlist technology, and (2) a novel risk assessment technique. For modelling the watchlist landscape, we propose a risk categorisation using the Doddington metric. We evaluate via experimental study on large-scale facial and fingerprint databases, the risks of impersonation and mis-identification in various screening scenarios. Other contributions include a study of approaches to designing a biometric-enabled watchlist for e-borders: a) risk control and b) improving performance of the e-border via integrating the interview supporting machines.

Emerging Biometrics: Deep Inference and Other Computational Intelligence

“…-Authentication and risk assessment machines for masstransit hubs and large public events. These machines operate under the umbrella of specific supporting infrastructure [96], [99], [100]. -Ambient assistants aims at social issues of critical importance: care management based on new cognitive paradigms [77].…”

Cognitive checkpoint: Emerging technologies for biometric-enabled watchlist screening

“…We use the term "weakly controlled" to indicate the most difficult part of the watchlist check that addresses the weaknesses of contemporary biometric technologies. For example, typical sources of image degradation include harsh ambient illumination conditions, mug-shots of facial images taken from passports, low-quality imaging devices, image compression and down sampling, out-of-focus acquisition, device or transmission noise, and motion blur [44], [49], [50], [51].…”

“…Misidentification remains a common occurrence due to shortcomings in current watchlist technologies and provides the principal motivation for this paper. Contemporary checkpoint is a cyber-physical computational platform that includes traveler authentication [23], [29], [48] using e-passport/ID [5], watchlist screening [9], [51], [86], concealed object detection [32], [60], interviewing [1], [4], [61], and risk assessment [49], [74], [76], [78], [91] under umbrella of justice and privacy issues [10], [15], [19]. In the International Air Transport Association (IATA) roadmap [36], four pillars of the checkpoint of the future are identified: computation intelligence, operations, infrastructure, and economic measures.…”

IoT Enabled Visual Recognition Model for Biometric Authentication

“…So that improve an automated border control algorithm is made for capturing the data clearly with modified comparative components. Kenneth Lai et.al [11], typically, watchlist have contained high quality images when compare of visitors either genuine or not in border control. Sometimes if watch-list own low quality images, them may happen a incidents drastically.…”