Abstract
A new human face recognition algorithm was proposed based on B2DPCA(bidirectional two dimensional principal component analysis) and ELM(extreme learning machine).This method was based on curvelet image decomposition of human faces and an improved dimensionality reduction technique.Discriminative feature sets were generated by using B2DPCA to train and test ELM classifier.The recognition accuracy can be improved by using this method.Extensive experiments were performed by using databases and results were compared with state of the existing techniques.The results showed recognition accuracy and minimal dependence on the number of prototypes were significantly improved by using B2DPCA and ELM algorithm.The local characteristics and global information based on curvelet decomposition are expected to apply to the recognition accuracy and speed of classification in the future.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
