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Zhou, S.K., Chellappa, R. and Moghaddam, B. (2004) Intra-Personal Kernel Space for Face Recognition. IEEE International Conference on Automatic Face and Gesture Recognition, Seoul, 19 May 2004, 235-240.
https://doi.org/10.1109/AFGR.2004.1301537

has been cited by the following article:

• TITLE: Human Face Super-Resolution Based on Hybrid Algorithm

  AUTHORS: Jinfeng Xia, Zhizheng Yang, Fang Li, Yuanda Xu, Nan Ma, Chunxing Wang

  KEYWORDS: Face Hallucination Super Resolution, Convolutional Network Hybrid Algorithm

  JOURNAL NAME: Advances in Molecular Imaging, Vol.8 No.4, September 14, 2018

  ABSTRACT: Aiming at the problems of image super-resolution algorithm with many convolutional neural networks, such as large parameters, large computational complexity and blurred image texture, we propose a new algorithm model. The classical convolutional neural network is improved, the convolution kernel size is adjusted, and the parameters are reduced; the pooling layer is added to reduce the dimension. Reduced computational complexity, increased learning rate, and reduced training time. The iterative back-projection algorithm is combined with the convolutional neural network to create a new algorithm model. The experimental results show that compared with the traditional facial illusion method, the proposed method can obtain better performance.