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An improved multi-scale face detection using convolutional neural network

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Abstract

In this paper, we introduce a deep learning (CNN) based method for face detection in an uncontrolled environment. The proposed method consists in developing a CNN architecture dedicated to the face detection tasks by combining both of global and local features at multiple scales. Our architecture is composed of two main networks: A region proposal network that generates a list of regions of interest (ROIs) and a second corresponds to a network that use these ROIs for classification into face/non-face. Both of them share the full-image convolution features of a pre-trained ResNet-50 model. Experimental study was conducted on the famous WIDER Face and FDDB databases. The obtained results proved the efficiency as well as the feasibility of the proposed method to deal with multi-scale face detection problems.

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Authors and Affiliations

  1. University of Sfax, MIRACL-FSEG, Sfax, Tunisia

    Sahar Dammak

  2. University of Sfax, MIRACL-ENET’COM, Sfax, Tunisia

    Hazar Mliki

  3. University of Sfax, MIRACL-FS, Sfax, Tunisia

    Emna Fendri

Authors
  1. Hazar Mliki
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  2. Sahar Dammak
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  3. Emna Fendri
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Correspondence to Sahar Dammak.

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Mliki, H., Dammak, S. & Fendri, E. An improved multi-scale face detection using convolutional neural network. SIViP 14, 1345–1353 (2020). https://doi.org/10.1007/s11760-020-01680-w

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  • DOI: https://doi.org/10.1007/s11760-020-01680-w

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