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Title: Simultaneous hallucination and recognition of low-resolution faces based on singular value decomposition
Authors: Jian, M
Lam, KM 
Keywords: Face hallucination
Low-resolution (LR) face recognition
Mapping model
Singular value decomposition (SVD)
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on circuits and systems for video technology, 2015, v. 25, no. 11, 7036137, p. 1761-1772 How to cite?
Journal: IEEE transactions on circuits and systems for video technology 
Abstract: In video surveillance, the captured face images are usually of low resolution (LR). Thus, a framework based on singular value decomposition (SVD) for performing both face hallucination and recognition simultaneously is proposed in this paper. Conventionally, LR face recognition is carried out by super-resolving the LR input face first, and then performing face recognition to identify the input face. By considering face hallucination and recognition simultaneously, the accuracy of both the hallucination and the recognition can be improved. In this paper, singular values are first proved to be effective for representing face images, and the singular values of a face image at different resolutions have approximately a linear relation. In our algorithm, each face image is represented using SVD. For each LR input face, the corresponding LR and high-resolution (HR) face-image pairs can then be selected from the face gallery. Based on these selected LR-HR pairs, the mapping functions for interpolating the two matrices in the SVD representation for the reconstruction of HR face images can be learned more accurately. Therefore, the final estimation of the high-frequency details of the HR face images will become more reliable and effective. The experimental results demonstrate that our proposed framework can achieve promising results for both face hallucination and recognition.
ISSN: 1051-8215
EISSN: 1558-2205
DOI: 10.1109/TCSVT.2015.2400772
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