Browsing Engineering and Informatics by Subject "Identification"
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Biometric of Intent: A New Approach Identifying Potential Threat in Highly Secured FacilitiesBiometric of Intent (BoI) is a Computer Vision (CV) automation, using Artificial Intelligence (AI) techniques, which presents a new approach that extends the reach of the classic biometric identification process. It provides an efficient mechanism which deters the threats raised by unknown individuals who have deceitful intentions and who aim to deploy unlawful operations such as terrorist attacks. In this context, our proposed BoI model is based on a framework constructed upon an automated machine learning facial expression analysis system which can assist law enforcement agencies who intend to deploy a systematic preventive security approach that aims to reduce the risk of potential unlawful attacks by rogue individuals through the evaluation of their emotional state in relation to their malicious intent.
Standardising the Capture and Processing of Custody ImagesCustody images are a standard feature of everyday Policing and are commonly used during investigative work to establish whether the perpetrator and the suspect are the same. The process of identification relies heavily on the quality of a custody image because a low-quality image may mask identifying features. With an increased demand for high quality facial images and the requirement to integrate biometrics and machine vision technology to the field of face identification, this research presents an innovative image capture and biometric recording system called the Halo. Halo is a pioneering system which (1) uses machine vision cameras to capture high quality facial images from 8 planes of view (including CCTV simulated), (2) uses high quality video technology to record identification parades and, (3) records biometric data from the face by using a Convolutional Neural Networks (CNN) based algorithm, which is a supervised machine learning technique. Results based on our preliminary experiments have concluded a 100% facial recognition rate for layer 34 within the VGG-Face model. These results are significant for the sector of forensic science, especially digital image capture and facial identification as they highlight the importance of image quality and demonstrates the complementing nature a robust machine learning algorithm has on an everyday Policing process.