A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 3 Issue 2
Apr.  2016

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Ningbo Hao, Haibin Liao, Yiming Qiu and Jie Yang, "Face Super-resolution Reconstruction and Recognition Using Non-local Similarity Dictionary Learning Based Algorithm," IEEE/CAA J. of Autom. Sinica, vol. 3, no. 2, pp. 213-224, 2016.
Citation: Ningbo Hao, Haibin Liao, Yiming Qiu and Jie Yang, "Face Super-resolution Reconstruction and Recognition Using Non-local Similarity Dictionary Learning Based Algorithm," IEEE/CAA J. of Autom. Sinica, vol. 3, no. 2, pp. 213-224, 2016.

Face Super-resolution Reconstruction and Recognition Using Non-local Similarity Dictionary Learning Based Algorithm

Funds:

This work was supported by China Postdoctoral Science Foundation (2015M582355), the Doctor Scientific Research Start Project from Hubei University of Science and Technology (BK1418), and National Natural Science Foundation of China (61271256).

  • One of the challenges of face recognition in surveillance is the low resolution of face region. Therefore many superresolution (SR) face reconstruction methods are proposed to produce a high-resolution face image from one or a set of low-resolution face images. However, existing dictionary learning based algorithms are sensitive to noise and very time-consuming. In this paper, we define and prove the multi-scale linear combination consistency. In order to improve the performance of SR, we propose a novel SR face reconstruction method based on nonlocal similarity and multi-scale linear combination consistency (NLS-MLC). We further proposed a new recognition approach for very low resolution face images based on resolution scale invariant feature (RSIF). A series of experiments are conducted on two public face image databases to test feasibility of our proposed methods. Experimental results show that the proposed SR method is more robust and computationally effective in face hallucination, and the recognition accuracy of RSIF is higher than some state-of-art algorithms.

     

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