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Patch-Wise Partial Face Recognition Using Convolutional Neural Network

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Abstract

Automatic face recognition still suffers from some problems in the real-world scenarios such as occlusion. Hence, identifying the face from its partial appearance is a challenging issue as yet. To address this, issue many methods have been proposed using traditional feature extraction techniques. In this paper, a partial face recognition problem has been tackled through utilizing patch-wise matching with Convolutional Neural Network (CNN). Firstly, a gallery images are divided into local patches, and each patch is regarded as an independent image. Then, AlexNet architecture is utilized for training image patches. The Instance-To-Class (ITC) matching technique using K-Nearest Neighbour (KNN) algorithm specifies the class of the facial test image based on patch prediction. The notable contributions of our work are two-folds: the first one is employing ITC technique for patch prediction and the last one is adopting a deep learning technique for feature extraction and handling partial occlusion problem. The achieved accuracies on two de-facto datasets show that our method outperforms several existing methods that use hand-designed feature descriptors.

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Authors and Affiliations

  1. Computer Science Department, Salahaddin University-Erbil, 44002, Erbil, Iraq

    Tarza Hasan Abdullah

  2. School of Science and Engineering, University of Kurdistan-Hewler, 44001, Erbil, Iraq

    Fattah Alizadeh

Authors
  1. Tarza Hasan Abdullah
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  2. Fattah Alizadeh
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Correspondence to Tarza Hasan Abdullah or Fattah Alizadeh.

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Tarza Hasan Abdullah, Alizadeh, F. Patch-Wise Partial Face Recognition Using Convolutional Neural Network. Opt. Mem. Neural Networks 31, 367–378 (2022). https://doi.org/10.3103/S1060992X22040087

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