Researchers at a Japanese university have developed a new photometric-based human face recognition technique that improves matching results in challenging lighting environments.Called OptiFuzz, the solution has been developed by researchers at Toyohashi University of Technology, reports uses an extended reflectance model to adjust the effect of lighting on human faces, thereby improving face detection and recognition results under a variety of illumination conditions.OptiFuzz has one variable, the illumination ratio, which is controlled by a fuzzy inference system (FIS). The researchers used a genetic algorithm (GA) to optimize the FIS rule to handle a range of illumination conditions.”To eliminate the effects of light, image contrast should be adjusted adaptively,” said researcher Bima Sena Bayu Dewantara. “To produce an invariant face appearance under backlighting, for example, cheeks need to be brightened, while the eyeballs must be kept dark. Such an adaptive contrast adjustment can be performed using the developed reflectance model, and we show that a combination of FIS and GA is very effective for implementing the model.”The team wsing the Yale B Extended and CAS-PEAL face databases for offline experiments and Viola-Jones Face Detector and the Mutual Subspace Method for online indoor and outdoor experiments.As noted by the magazine, their algorithm could outperform existing methods for recognizing a specific person under variable lighting conditions with a significantly improved computation time. The results also showed that illumination invariant images could improve face detection performance.”By just adding this contrast adjustment to present face recognition systems, we can largely improve the accuracy and performance of face detection and recognition,” said professor Jun Miura. “Moreover, this adjustment runs in real time, and therefore, it is appropriate for real-time applications such as robot and human-interaction systems.”