Biometrics - recognition of persons based on how they look or behave, is the main subject of research at the Chair of Biometric Pattern Recognition (BPR) of the Services, Cyber Security and Safety Group (SCS) of the EEMCS Faculty at the University of Twente. Examples are finger print recognition, iris and face recognition. A relatively new field is 3D face recognition based on the shape of the face rather that its appearance. This paper presents a method for 3D face recognition developed at the Chair of Biometric Pattern Recognition (BPR) of the Services, Cyber Security and Safety Group (SCS) of the EEMCS Faculty at the University of Twente and published in 2011. The paper also shows that noteworthy performance gains can be obtained by optimisation of an existing method. The method is based on registration to an intrinsic coordinate system using the vertical symmetry plane of the head, the tip of the nose and the slope of the nose bridge. For feature extraction and classification multiple regional PCA-LDA-likelihood ratio based classifiers are fused using a fixed FAR voting strategy. We present solutions for correction of motion artifacts in 3D scans, improved registration and improved training of the used PCA-LDA classifier using automatic outlier removal. These result in a notable improvement of the recognition rates. The all vs all verification rate for the FRGC v2 dataset jumps to 99.3% and the identification rate for the all vs first to 99.4%. Both are to our knowledge the best results ever obtained for these benchmarks by a fairly large margin.
|Number of pages||11|
|Publication status||Published - Nov 2015|
- Face Recogntion