Multimedia Tools and Applications

, Volume 76, Issue 5, pp 6641–6661 | Cite as

Converted-face identification: using synthesized images to replace original images for recognition

  • Changbin Shao
  • Xiaoning Song
  • Xin Shu
  • Xiao-Jun Wu


The changes in appearance of faces, usually caused by pose, expression and illumination variations, increase data uncertainty in the task of face recognition. Insufficient training samples cannot provide abundant multi-view observations of a face. To address this issue, many pioneering works focus on generating virtual training images for better recognition performance. However, the issue also exists in a test set where a test image only conveys a split-second representation of a face and cannot cover more comprehensive features. In this paper, we propose a new face synthesis method for face recognition. In the proposed pipeline, we synthesize a virtual image using both the original image and its corresponding mirror one. Note that, we apply this technique both to the training and test sets. Then we use the newly generated training and test images to replace the original ones for face recognition. The aim is to increase the similarity between a test image and its corresponding intra-class training images. This proposed method is effective and computationally efficient. In order to verify this, we tested our system using multiple face recognition methods in terms of the recognition accuracy, based on either the synthesized images or original images. The methods used in the paper include statistical subspace learning algorithms and representation-based classification approaches. Experimental results obtained on FERET, ORL, GT, PIE and LFW show that the proposed approach improves the face recognition accuracy, especially on faces with left-right pose variations.


Data uncertainty Mirror image Virtual image Representation-based classification Subspace learning Face recognition 


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Changbin Shao
    • 1
  • Xiaoning Song
    • 2
    • 3
  • Xin Shu
    • 1
  • Xiao-Jun Wu
    • 2
  1. 1.School of Computer Science and EngineeringJiangsu University of Science and TechnologyZhenjiangChina
  2. 2.School of Internet of Things EngineeringJiangnan UniversityWuxiChina
  3. 3.Centre for Vision, Speech and Signal ProcessingUniversity of SurreyGuildfordUK

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