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Facial Expression Recognition and Analysis of Interclass False Positives Using CNN

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Advances in Information and Communication (FICC 2019)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 70))

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Abstract

In this paper, the performance of Facial Expression Recognition (FER) using Deep Convolutional Neural Network (DCNN) model is evaluated. The expressions include Angry, Disgust, Fear, Happy, Sad, Surprise, and Neutral. In addition to performance evaluation, the analysis on Interclass false positives are also discussed which helps to analyze the underlying challenges to improve the model. All classifiers give low performance on Fer2013 datasets. DCNN gives 54.46% accuracy on test and 89.52% on training set, whereas, in case of different kernels of Support Vector Machines (SVM), the highest accuracy is 45% using cubic kernel on training set. Experiments show that certain facial expressions have more false positives and few of them are very dominant. In case of Disgust expression, it has Angry as dominant false positive. Based on false positives analysis, binary classifiers can be trained to improve the accuracy of the expressions with dominant false positives. Experiments on Fer2013 dataset confirms that the accuracy of expression Disgust is improved by piping the binary classifiers, Disgust vs. Angry, to existing DCNN of multi-class.

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Notes

  1. 1.

    http://www.kasrl.org/jaffe.html.

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Acknowledgements

This research is supported by University of Balochistan under the project UBRF with grant number UOB/ORIC/17/UBRF-17/022, and Higher Education Commission of Pakistan (HEC).

Author information

Authors and Affiliations

  1. Department of CS and IT, University of Balochistan, Quetta, Pakistan

    Junaid Baber, Maheen Bakhtyar, Kafil Uddin Ahmed & Waheed Noor

  2. University of Grenoble Alpes, Grenoble, France

    Varsha Devi

  3. Department of Computer Science, Habib University, Karachi, Pakistan

    Abdul Sammad

Authors
  1. Junaid Baber
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  2. Maheen Bakhtyar
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  3. Kafil Uddin Ahmed
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  4. Waheed Noor
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  5. Varsha Devi
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  6. Abdul Sammad
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Corresponding author

Correspondence to Junaid Baber .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, UK

    Rahul Bhatia

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Baber, J., Bakhtyar, M., Uddin Ahmed, K., Noor, W., Devi, V., Sammad, A. (2020). Facial Expression Recognition and Analysis of Interclass False Positives Using CNN. In: Arai, K., Bhatia, R. (eds) Advances in Information and Communication. FICC 2019. Lecture Notes in Networks and Systems, vol 70. Springer, Cham. https://doi.org/10.1007/978-3-030-12385-7_5

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