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http://dx.doi.org/10.9717/kmms.2018.21.12.1467

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)
Publication Information
Journal of Korea Multimedia Society / v.21, no.12, 2018 , pp. 1467-1472 More about this Journal
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
Face Recognition; Intelligent Video Analysis Method; CNN; Multi-scale CNN;
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