Pose-Invariant Face Synthesis and Recognition via Sparse Coding and Symmetrical Information

Abstract:

Pose variation which brings illumination change, occlusion and non-linear scale variation, dramatically drops the performance of face recognition systems. In this paper, we propose a novel pose invariant face recognition method, in which we build a joint sparse coding scheme to predict face images from a certain pose to another. By introducing autoregressive regularization and symmetric information, our algorithm could achieve high robustness to local misalignment and large pose differences. Besides, we propose a new coarse pose estimation algorithm by collaborative representation classifier, which is very fast and enough accurate for our synthesis algorithm. The experiment results on multi-pose subsets of CMU-PIE and FERET database show the efficiency of the proposed method on multi-pose face recognition.

• Periodical: Applied Mechanics and Materials (Volume 437)
• Pages: 894-900
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.437.894
• Online since: October 2013
• Authors: Shuai Zhang, Hai Rui Wang*, Xiao Li He
• Keywords: Face Synthesis, Multi-Pose Face Recognition, Sparse Coding

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