FLDA Improved by Adaptive Generic Learning Framework for Face Recognition
Sun Weiqian · Video Engineering · 2014
For the issue that traditional Fisher linear discriminative analysis algorithm could not extract features due to its scattering matrix within class is zero in face recognition with single training sample per person,a face recognition algorithm based on FLDA improved by adaptive generic learning framework is proposed. Firstly,a suitable generic training sample set is selected and its scattering matrix within class and mean vectors are computed.Then,scattering matrix within-class and between classes are predicted by bilinear representation algorithm,which has settled the problem of its scattering matrix within class is zero. Finally,FLDA is used to extract features and nearest neighbour classifier is used to finish face recognition. The effectiveness of proposed algorithm is verified by experiments on the two common databases Yale and FERET. Experimental results show that proposed algorithm has better recognition efficiency than several advanced single training sample face recognition algorithms.