Probabilistic Elastic Part Model: A Pose-Invariant Representation for Real-World Face Verification

IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 918-930, 2018.

Cited by: 12|Bibtex|Views24|DOI:https://doi.org/10.1109/TPAMI.2017.2695183
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Other Links: dblp.uni-trier.de|pubmed.ncbi.nlm.nih.gov|academic.microsoft.com

Abstract:

Pose variation remains to be a major challenge for real-world face recognition. We approach this problem through a probabilistic elastic part model. We extract local descriptors (e.g., LBP or SIFT) from densely sampled multi-scale image patches. By augmenting each descriptor with its location, a Gaussian mixture model (GMM) is trained to ...More

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