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Human emotion recognition based on facial expressions via deep learning on high-resolution images

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Abstract

Detecting human emotion based on facial expression is considered a hard task for the computer vision community because of many challenges such as the difference of face shape from a person to another, difficulty of recognition of dynamic facial features, low quality of digital images, etc. In this paper, we propose a face-sensitive convolutional neural network (FS-CNN) for human emotion recognition. The proposed FS-CNN is used to detect faces on large scale images then analyzing face landmarks to predict expressions for emotion recognition. The FS-CNN is composed form two stages, patch cropping, and convolutional neural networks. The first stage is used to detect faces in high-resolution images and crop the face for further processing. The second stage is a convolutional neural network used to predict facial expression based on landmarks analytics, it was applied on pyramid images to process scale invariance. The proposed FS-CNN was trained and evaluated on the UMD Faces dataset. High performance was achieved with a mean average precision of about 95%.

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Acknowledgements

The authors wish to acknowledge the approval and the support of this research study by the grant N° ENG-2019-1-10-F-1113 from the Deanship of the Scientific Research in Northern Border University, Arar, KSA.

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Authors and Affiliations

  1. Electrical Engineering Department, College of Engineering, Northern Border University, Arar, Saudi Arabia

    Yahia Said & Mohammad Barr

  2. Laboratory of Electronics and Microelectronics (LR99ES30), Faculty of Sciences of Monastir, University of Monastir, Monastir, Tunisia

    Yahia Said

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  1. Yahia Said
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Correspondence to Yahia Said.

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Said, Y., Barr, M. Human emotion recognition based on facial expressions via deep learning on high-resolution images. Multimed Tools Appl 80, 25241–25253 (2021). https://doi.org/10.1007/s11042-021-10918-9

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