Facial or face recognition analyses characteristics of a person's face image input through a camera and can be broadly classified into static and dynamic/video matching. Facial recognition systems at a very high level work by recognising a human face from scene and extract it. The system measures overall facial structure, distances between eyes, nose, mouth, and jaw edges, then compares these nodal points to the nodal points computed from a database of pictures in order to find a match.
Facial recognition is used for both identification (1:n) and verification (1:1).
HRS has been instrumental in introducing dynamic facial recognition across the aviation sector.
MFlow Journey was developed for and with airport operators to measure, manage and ultimately remove bottlenecks to optimise passenger flow. MFlow Journey enables airports to maximise non-aeronautical revenues, improve customer service through reduced passenger queue times and ensure efficient staffing levels.
The system works by capturing anonymous facial images of passengers entering a designated area. Using facial recognition technology ensures the capture of the maximum number of faces.
The anonymous images are used to track passengers through agreed way-points including check-in, security and passport control. The accurate time taken between way-points gives split and cumulative timings. Alerts are created if journey times are outside set goals to easily identify emerging operational issues before they impact the operation.
Our facial recognition technology is based on neural computing principles, which combine the advantages of neural and elastic networks. HRS smart surveillance platforms are able to extract faces from a moving or a static environment and run verification checks against those on watch lists and central databases.