Very low resolution face recognition via super-resolution based on extreme learning machine
Lu Ta · Journal of Computer Applications · 2016
The very low-resolution image itself contains less discriminant information and is prone to be interfered by noise, which reduces the recognition rate of the existing face recognition algorithm. In order to solve this problem, a very low resolution face recognition algorithm via Super-Resolution( SR) based on Extreme Learning Machine( ELM) was proposed.Firstly, the sparse expression dictionary of Low-Resolution( LR) and High-Resolution( HR) images were learned from sample base, and the HR image could be reconstructed due to the manifold consistency of LR and HR expression coefficients.Secondly, the ELM model was built on the HR reconstructed images, the connection weight of feedforward neural networks was obtained by training. Lastly, the ELM was used to predict the category attribute of the very low-resolution image. Compared with traditional face recognition algorithm based on Collaborative Representation Classification( CRC), the experimental results show that the recognition rate of the proposed algorithm increases by 2% upon the reconstructed HR images. At the same time, it greatly shortens the recognition time. The simulation results show that the proposed algorithm can effectively solve face recognition problem caused by limited discriminant information in very low-resolution image and it has better recognition ability.