Human face recognition based on multidimensional PCA and extreme learning machine

In this work, a new human face recognition algorithm based on bidirectional two dimensional principal component analysis (B2DPCA) and extreme learning machine (ELM) is introduced. The proposed method is based on curvelet image decomposition of human faces and a subband that exhibits a maximum standard deviation is dimensionally reduced using an improved dimensionality reduction technique. Discriminative feature sets are generated using B2DPCA to ascertain classification accuracy. Other notable contributions of the proposed work include significant improvements in classification rate, up to hundred folds reduction in training time and minimal dependence on the number of prototypes. Extensive experiments are performed using challenging databases and results are compared against state of the art techniques.

► An efficient face recognition technique using curvelet features is proposed. ► The curvelet features are dimensionally reduced using B2DPCA. ► These features are input to an ELM to analytically learn an optimal model. ► We achieve improved recognition at faster rate against existing techniques. ► Our method is independent of training data size and the number of hidden neurons.