Simultaneous Hallucination and Recognition of Low-Resolution Faces Based on Singular Value Decomposition

Muwei Jian, Kin Man Lam

Research output: Journal article publicationJournal articleAcademic researchpeer-review

76 Citations (Scopus)

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.
Original languageEnglish
Article number7036137
Pages (from-to)1761-1772
Number of pages12
JournalIEEE Transactions on Circuits and Systems for Video Technology
Volume25
Issue number11
DOIs
Publication statusPublished - 1 Nov 2015

Keywords

  • Face hallucination
  • low-resolution (LR) face recognition
  • mapping model
  • singular value decomposition (SVD)

ASJC Scopus subject areas

  • Media Technology
  • Electrical and Electronic Engineering

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