Facial recognition has received significant attention in the last few years and is increasingly being used for both identification (1:n) and verification (1:1) on large identity projects across the public and private sector. Facial 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 then measures nodal points on the face, distance between eyes, shape of the cheekbones and other distinguishable features. These nodal points are then compared to the nodal points computed from a database of pictures in order to find a match.
HRS has extensive experience of developing and delivering solutions across both static and dynamic/video based facial recognition. Our facial recognition solutions (e.g. MFlow Journey) are helping increase security and identity assurance across a variety of sectors including large government identity schemes, aviation, retail, law enforcement, healthcare, prisons etc. Our team of biometric engineers has been at the forefront of developing modular, scalable facial recognition systems allowing implementation in small systems through to very large scale government identity projects. The modularity of our solution architecture provides the flexibility to adjust its configuration to an agency/end user's changing needs. In other words our solutions can be scaled up easily over time, saving unnecessary up-front costs.
HRS benefits from strategic partnership with some of the leading names in the industry and has been actively involved in developing cutting edge commercial applications across both the public and private sector. More recently HRS has been instrumental in introducing dynamic facial recognition across the aviation sector. HRS continues to explore new solutions in the domain and our team of biometric engineers is currently working on developing a range of commercially robust applications around both static and dynamic facial recognition.
Facial recognition or face recognition as it is often referred to as, analyses characteristics of a person's face image input through a camera. It measures overall facial structure, distances between eyes, nose, mouth, and jaw edges. These measurements are retained in a database and used as a comparison when a user stands before the camera.
One of the strongest positive aspects of facial recognition is that it is non-intrusive. Verification or identification can be accomplished from two feet away or more, without requiring the user to wait for long periods of time or do anything more than look at the camera. 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.
Facial recognition is currently used in verification only systems with a good deal of success. The system locates the user's face and performs matches against the claimed identity or the facial database. To prevent a face mould from faking out the system, many systems now require the user to smile, blink, or move in a way that is human before verifying. Some of the existing applications for facial recognition technology include: