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According to Roberto Mariani, a research staffer on the LIT's Media Engineering Program, the technology is also robust enough to offer a face recognition system for mobile applications such as robotic vehicles.
LIT is a national research and development institute controlled by the Agency for Science and Technology. Showcased at the CommunicAsia exhibition last week, the technology addresses real world conditions that seriously affect the accuracy of current face recognition technology, Mariani said.
Mariani explained that two of these conditions are varied lighting conditions and varied face poses (the orientation of the face relative to the camera). The technology has been developed to take account of varied expressions and the presence or absence of facial artifacts such as glasses, hairstyles or moustaches. These conditions can affect either the coding process to create inaccurate database images, or more significantly, the matching process when the system encounters a less-than-ideal image to be matched against the database.
Mariani said the team originally sought to tackle the problem by exploring how to enhance normalisation techniques to provide good matches against database images. Conventional face recognition systems already use various normalisation techniques. Mariani described this as an "extremely difficult" endeavor, as there were too many real world lighting and face pose variations for any one system to adequately accommodate.
The solution the LIT team developed is based on what it describes as a unique face synthesis procedure. The technology basically synthesises multiple images from a single image to produce a variety of images in different poses and lighting conditions.
By boosting the number of realistic images that the face recognition system can match an image against, the premise is that at least one of the synthesised images will register a correct match with the entry in the database. Or, the technology can be used to increase the number of models in the face database to offer a richer set of images to be matched against. "The synthesis layer basically brings more knowledge and intelligence to the face recognition system," explained Mariani.
With multiple image extrapolation as the foundation of this technology, the LIT team also incorporated faster matching algorithms to speed up recognition time, by enabling rapid and accurate sifting of entries in the image database.
This technology outperformed conventional face recognition systems in a trial heterogeneous database of 175 images in a wide variety of lighting conditions, poses and picture quality.
According to the researcher, the solution is also robust enough to offer a face recognition system for mobile applications such as robotic vehicles.
LIT is seeking opportunities to work closely with system integrators and original equipment manufacturers to address key application areas such as surveillance, access control, mobile commerce or toy applications.