Face Recognition with Incomplete Data by Deep Convolutional Neural Network (DCNN)

Articles in Press

Document Type : Original Article

Authors

1 Department of Computer Engineering, Technical and Vocational University (TVU), Tehran, Iran

2 Department of Computer Engineering, Shahriar Institute of Higher Education, Astara, Iran

3 Department of Electronics, Islamic Azad University, Astara Branch, Astara, Iran

Abstract

The human face is a dynamic entity influenced by various factors that give rise to different facial expressions. Face recognition algorithms encounter challenges such as inherent and random factors causing facial appearance variations, incomplete data in the database, database size, differences in image dimensions, and changes in facial expressions. Addressing these challenges can expand the application range of facial recognition techniques. In this study, we propose a method that utilizes a deep convolutional neural network to enhance face recognition in the presence of incomplete data. The proposed method consists of several distinct steps. Firstly, primary data is selected and extracted from the database, followed by preprocessing the information through filtering, histogram transformation, and edge detection. Subsequently, crucial facial landmarks are extracted for each image. The output of this step serves as input to the bee optimization algorithm, which facilitates the selection of relevant features and optimizes them for recognition. Finally, a deep convolutional neural network is employed for face recognition, encompassing training and testing stages. We conducted simulations in the MATLAB environment to evaluate the proposed method and assess using accuracy, correctness, and criteria coverage criteria. The results demonstrated an accuracy of 96.11%, indicating improved face recognition compared to recent works and cost reductions in the overall recognition process.

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Soft Computing Journal

Articles in Press, Accepted Manuscript
Available Online from 08 October 2023

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History

  • Receive Date: 12 April 2023
  • Revise Date: 06 July 2023
  • Accept Date: 07 October 2023

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