A facial recognition system that recognizes racial differences will yield more accurate results than a “one-size-fits-all” model, researchers from the University of Surrey report in the journal Pattern Recognition. The team found that a 3-D morphing face model trained to specifically recognize black, white and Asian faces in 2-D images performed better identifying faces at different angles and in different lighting scenarios than previous models.
“The main target of our paper is to deal with extreme pose variations in face recognition. We found that multi-modal 3D face models constructed using face attributes (e.g. race, gender age or expression) can recover the 3D face from a single 2D image better than a unified model, hence improve the performance in pose-invariant face recognition,” explained lead author Zhenhua Feng, from University of Surrey’s Centre for Vision, Speech and Signal Process, in an email to Forensic Magazine.
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Source: Forensics Magazine