Browsing Health Studies by Subject "Machine learning"
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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.