Face Recognition with Nonlinear Dimensionality Reduction Based on Locality Constrained Dictionary Learning
Jiang Ke-hu · Science Technology and Engineering · 2013
For the problem that existed nonlinear dimensionality reduction( NLDR) algorithms can not be applied in large-scale data sets in the real world due to its high complexity,nonlinear dimensionality reduction based on locality constrained dictionary learning( LCDL-NLDR) is proposed. Firstly,training and unknown dataset is naturally embedded to tiny inner manifold reconstructed by some potential landmarks. Then,closely atomic sets consist of mark points will be learned effectively by locality constrained dictionary learning( LCDL) algorithm in nonlinear manifold. Finally,nearest neighbor classifier is applied to finish face recognition. The effectiveness and robustness of proposed method has been verified by experiments on extended YaleB and CMU PIE face databases. Comparison with several latest dictionary learning algorithms shows that proposed algorithm has improving embedded quality,getting higher recognition accuracy,and reducing the complexity of NLDR algorithm clearly.