Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
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Low Resolution Rate Face Recognition Based on Multi-scale CNN

  • Wang, Ji-Yuan (Dept. of Information and Communication Engineering, Tongmyong University) ;
  • Lee, Eung-Joo (Dept. of Information and Communication Engineering, Tongmyong University)
  • Received : 2018.10.08
  • Accepted : 2018.10.31
  • Published : 2018.12.31
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Abstract

For the problem that the face image of surveillance video cannot be accurately identified due to the low resolution, this paper proposes a low resolution face recognition solution based on convolutional neural network model. Convolutional Neural Networks (CNN) model for multi-scale input The CNN model for multi-scale input is an improvement over the existing "two-step method" in which low-resolution images are up-sampled using a simple bi-cubic interpolation method. Then, the up sampled image and the high-resolution image are mixed as a model training sample. The CNN model learns the common feature space of the high- and low-resolution images, and then measures the feature similarity through the cosine distance. Finally, the recognition result is given. The experiments on the CMU PIE and Extended Yale B datasets show that the accuracy of the model is better than other comparison methods. Compared with the CMDA_BGE algorithm with the highest recognition rate, the accuracy rate is 2.5%~9.9%.

Keywords

MTMDCW_2018_v21n12_1467_f0001.png 이미지

Fig. 1. Multi-scale Convolutional Neural Network (MSCNN) algorithm training and testing process.

MTMDCW_2018_v21n12_1467_f0002.png 이미지

Fig. 2. Multi-scale convolutional neural network structure.

MTMDCW_2018_v21n12_1467_f0003.png 이미지

Fig. 3. Different resolution face recognition results on CMU PIE database.

Table 1. Layer parameter list

MTMDCW_2018_v21n12_1467_t0001.png 이미지

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