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Face recognition using patch manifold learning across plastic surgery from a single training exemplar per enrolled person

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Abstract

Although numerous methods have been developed for human face recognition, little investigation is focused on the human face recognition across plastic surgery and also single-exemplar face recognition. In this article, we present a new face recognition algorithm using patch manifold learning under plastic surgery conditions when only a single training exemplar per enrolled person exists. In the presented method, a face image is divided into a collection of patches which have no overlapping that are considered as a manifold. Then, we formulate face recognition under plastic surgery conditions using a single exemplar of each person as a problem of manifold–manifold matching to maximize the margin of manifold patches. A complete experimental investigation is done using the database of plastic surgery, AR and also FERET face databases. Experimental results indicate the superiority of the presented algorithm for face recognition in single-sample databases.

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Authors and Affiliations

  1. Department of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

    Mahlagha Ebadi

  2. Department of Computer Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran

    Hamidreza Rashidy Kanan & Mohammad Kalantari

Authors
  1. Mahlagha Ebadi
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  2. Hamidreza Rashidy Kanan
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  3. Mohammad Kalantari
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Correspondence to Hamidreza Rashidy Kanan.

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Ebadi, M., Rashidy Kanan, H. & Kalantari, M. Face recognition using patch manifold learning across plastic surgery from a single training exemplar per enrolled person. SIViP 14, 1071–1077 (2020). https://doi.org/10.1007/s11760-020-01642-2

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  • DOI: https://doi.org/10.1007/s11760-020-01642-2

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