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Resolution Invariant Face Recognition

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Computer Vision and Machine Intelligence

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 586))

Abstract

Face images recorded from security cameras and other similar sources are generally of low-resolution and bad quality. There are many recent face recognition models which extract face features/encodings using deep neural networks (DNNs) and give very good results when tested against images with higher resolution (HR). Moreover, the performance of these types of algorithms deteriorates to a great extent for images with low-resolution (LR). To reduce the shortcoming, we used convolution neural network (CNN) architecture along with the combination of super-resolution (SR) technique during the pre-processing steps to achieve the comparable results on the state-of-the-art techniques. The proposed method can be outlined in 4 steps: Face retrieval, image pre-processing and super-resolution, training the model, and face detection/classification. The dataset used for this study is a publicly available with name Face Scrub Dataset, subset of this dataset is used containing 20050 images of 229 people for the experiments.

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Authors and Affiliations

  1. Department of Information Technology, IIIT Allahabad, Prayagraj, India

    Priyank Makwana, Satish Kumar Singh & Shiv Ram Dubey

Authors
  1. Priyank Makwana
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  2. Satish Kumar Singh
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  3. Shiv Ram Dubey
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Corresponding author

Correspondence to Priyank Makwana .

Editor information

Editors and Affiliations

  1. Computer Vision Laboratory, University of Sassari, Alghero, Sassari, Italy

    Massimo Tistarelli

  2. Computer Vision and Biometrics Lab, Department of Information Technology, Indian Institute of Information Technology Allahabad, Prayagraj, India

    Shiv Ram Dubey

  3. Computer Vision and Biometrics Lab, Department of Information Technology, Indian Institute of Information Technology, Allahabad, India

    Satish Kumar Singh

  4. University of Münster, Münster, Germany

    Xiaoyi Jiang

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Makwana, P., Kumar Singh, S., Ram Dubey, S. (2023). Resolution Invariant Face Recognition. In: Tistarelli, M., Dubey, S.R., Singh, S.K., Jiang, X. (eds) Computer Vision and Machine Intelligence. Lecture Notes in Networks and Systems, vol 586. Springer, Singapore. https://doi.org/10.1007/978-981-19-7867-8_58

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